K-means algorithm is a common and effective clustering algorithm based on partition . To solve the problem of sensitivity of initial cluster centers , the most frequently used method is searching optimal initial cluster centers by hierarchically initializing . In many applications , attribute and relationship data areavailable , carrying complementary information about real world entities . In such cases , a joint analysis of both types of data can yield more accurate results than classical clustering algorithms that either use only attribute data or only relationship ( graph ) data . The Connected k-Center ( CkC ) has been proposed as the first joint cluster analysis model to discover k clusters which are cohesive on both attribute and relationship data . Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets . Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation , in comparison with four other popular clustering algorithms . ConclusionFor the first time partial mixture model is successfully extended to time-course data analysis . The robustness of our partial regression clustering algorithm proves the suitability of the combination of both partial mixture model and minimum distance estimator in this field . We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge , but also presents interesting new hypotheses during interpretation of clustering results . Traditional collaborative filtering algorithm does not take into account the user ’s interest factors , at the same time , it has the problem of sparse data , poor scalability and so on , which directly affects the quality of the recommendation . This paper proposes a collaborative filtering recommendation algorithm based on user interest clustering . Firstly , according to the user ’s existing score , a method is used to compute the interest degree of the user . Through clustering the two phases of dense data subset and the whole data set , the clustering effect of extreme sparse data sets are improved . At the same time , because of fuzziness of graph clustering , this thesis retains a certain diversity in the process of user interest clustering , so as to avoid convergence too fast when cold start . This method is verified through the real social network data , and the experimental results show that this algorithm can effectively solve the problems such as data sparseness and cold start phenomenon . The theory is applied to the address block location problem . A multiple locator algorithm employing a machine-printed address block locator , a handwritten address block locator , and a clustering-based locator is created and tested on a database of 1,100 mail piece images , in two separate experiments . The experimental results show that combining location results using the theory provides location performance that excels any individual locator performance , and therefore is a viable approach to document object location problems . CSO is a latest meta-heuristic algorithm based on the behavior of cats . In this paper , we present a clustering-based test case prioritization technique . The method consists of clustering the test cases based on fault detected by test cases . Abstract Due to high dimensionality and multiple variables , unsupervised classification of 12-lead ECG signals involves challenges and difficulties . In order to automatically discover unknown physiological features from raw multivariate signals and detect abnormal cardiac activities of a subject , we proposed an unsupervised classification scheme of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering . After filtering and segmentation , each ECG sample is converted into a wavelet tensor by the Discrete Wavelet Packet Transform ( DWPT ) . Such an organization presents a basic conundrum : Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer . It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem . However , there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity , a global property unrelated to local clustering . We present measures to quantify the shadowing effect , and with Faceted Clustering we introduce an algorithm that optimizes the identified principles . The key idea of Faceted Clustering is a dynamic , user-controlled reorganization of a clustering , similar to a faceted navigation system . We report on evaluations using the AMBIENT corpus and demonstrate the potential of our approach by a comparison with two well-known clustering search engines . But it adds complexity for database querying and updating . We improve it and propose CET ( Clustering Extension Table ) . There are several tables in CET , including BT ( Basic Table ) , CT ( Clustering Table ) , CMT ( Metadata Table for CT ) , ET ( Extension Table ) and EMT ( Metadata Table for ET ) . So , using Hill Climbing Automatic Clustering to function identify about movement variance from each phase formation cluster and analyze pattern so that cluster total will be formed in automatic . The use of text mining , clustering by CLHM and Hill Climbing Automatic Clustering process to make easy user because get result clustering precisely and automaticaly . The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances . Once this embedding has been produced , then the aforementioned tasks become straight - forward : face verification simply involves thresholding the distance between the two embeddings ; recognition becomes a k - NN classification problem ; and clustering can be achieved using off - the - shelf techniques such as k - means or agglomerative clustering . Previous face recognition approaches based on deep networks use a classification layer trained over a set of known face identities and then take an intermediate bottleneck layer as a representation used to generalize recognition beyond the set of identities used in training . document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . In the rest of the paper , we begin by explaining the difference between semantic clustering and intermediate representation learning ( s : related ) , which separates our method from the majority of work in unsupervised deep learning . We then describe the theoretical foundations of IIC in statistical learning ( s : method ) , demonstrating that maximising mutual information between pairs of samples under a bottleneck is a principled clustering objective which is equivalent to distilling their shared abstract content ( co - clustering ) . We propose that for static images , an easy way to generate pairs with shared abstract content from unlabelled data is to take each image and its random transformation , or each patch and a neighbour . Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion . Agglomerative clustering and partially ordered sets of HOG features have been used to group images , and exemplars define a group as a set of random transformations applied to a single image . Note the latter does not scale easily , in particular to image segmentation where a single image would call for 40k classes . This is corrected by the conditional entropy component , which encourages deterministic one - hot predictions . For example , even for the degenerate case of identical pairs , the IIC objective encourages a deterministic clustering function ( i.e. is a one - hot vector ) as this results in null conditional entropy . Secondly , the objective of IIC is to find what is common between two data points that share redundancy , such as different images of the same object , explicitly encouraging distillation of the common part while ignoring the rest , i.e. instance details specific to one of the samples . [ 33 ] unrolls the iterations forming the sparse codes and optimizes end - to - end the involved parameters using a clustering objective as loss function [ 33 ] . The proposed framework is further augmented by clustering objectives applied to intermediate representations , which act as feature regularization within the unrolled optimization . They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts . Our method differs in that we use convolutional neural networks rather than unrolling a sparse coding optimization . In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network . In contrast we employ a k - means like clustering objective which simplifies the optimization significantly and does not require a recurrent procedure . For spatial clustering the dimension of the matrix µ is K × C. ( C ) Channel Co - Clustering : This regularizer groups the channels of different samples directly , thus co - clustering samples and filters . We expect this type of regularization to model re - occurring Algorithm 1 : Most of the recent approaches use Convolutional Neural Networks ( CNNs ) to detect body parts and relationships between them in an end - to - end manner , then use assignment algorithms to form individual skeletons . Pischulin et al . used deep features for joint prediction of part locations and relations between them , then performed correlation clustering . Even though does n’t use person detections , it is very slow due to proposed clustering algorithm and processing time is in the order of hours . In contrast , our novel formulation relies on fully connected model to deal with unknown number of people per image and body parts per person . The Minimum Cost Multicut Problem , known in machine learning as correlation clustering , has been used in computer vision for image segmentation but has not been used before in the context of pose estimation . It is known to be NP - hard . This strategy can achieve a joint maximum purity for all the split - nodes when training a random forest . Image super - resolution can be performed based on clustering / classification , according to the recent emerging clustering - regression stream [ reference ][ reference ][ reference ] , and the JMPF scheme can achieve remarkable performance on both the classification and regression tasks . Therefore , JMPF is applied to single - image super - resolution in this paper . Mean shift clustering vs ground truth clustering Since the output of our network needs to be clustered into discrete instances , the clustering method can potentially influence the accuracy of the overall instance segmentation . In this experiment , we measure this influence by clustering with our adapted mean shift clustering algorithm versus thresholding around the mean embeddings over the ground truth instance masks , as explained in section [ reference ] ) . subsection : Results and discussion The objective of the paper is to give the user internal information contained in the data by using only the binary class-information data . A self organizing map ( SOM ) is used as the main tool for this purpose . Our method is to tune the weight of the elements of the data so that the data of the same category tend to be mapped in the near points on the SOM , and the separation of different categories can be carried out successfully . The purpose of the proposed system is to create autonomous systems that can learn independently and cooperate to provide a better decision of the phoneme classification . The basic SOM variant is a hybrid model of SOM and Genetic Algorithm ( GA ) using a growing incremental technique to adapt the map structure and extra information in the map units to optimise the map codebooks . The hierarchical evolutionary learning algorithm classifies data according to tow hierarchical levels ensuring the representation of the hierarchical relations of the data . In this paper , we propose a two-level self-organizing map ( SOM ) that automatically responds to the users ' questions on Internet , and helps them to find their answer for themselves by browsing the map hierarchically . The method consists of keyword clustering SOM that reduces a variable length question to a normalized vector , and document classification SOM that classifies the question into an answer class . The final map is also used for browsing . In this paper , we propose an assistant system which responds to the users questions automatically and helps them browse the Hanmail Net FAQ ( Frequently Asked Question ) conceptually . This system uses two-level self-organizing map ( SOM ) : the keyword clustering SOM and document classification SOM . The keyword clustering SOM reduces a variable length question to a normalized vector and the document classification SOM classifies the question into an answer class . In this paper , a novel self-organizing map ( SOM ) named “ One-D-R-A-G-SOM ” is proposed . It is a kind of one dimensional ring type growing SOM using asymmetric neighborhood function . As the topology of one dimensional ring type feature map is more suitable to increase or decrease the number of units , and the disorder of the map is available to be solved by the asymmetric neighborhood function , the proposed model gives priority of learning performance to the conventional two dimensional growing SOM . An asymmetric neighborhood function was proposed by Aoki and Aoyagi to instead of symmetric neighborhood function in conventional Kohonen 's self-organizing map ( SOM ) to avoid topological twist of the order of units during training process . Meanwhile , a one dimensional ring type growing SOM was proposed by Ohta and Saito to reduce the unnecessary increasing of units of conventional 2-D growing SOM . In this paper , we adopt the asymmetric neighborhood to a parameterless growing SOM ( PL-G-SOM ) proposed by Kuremoto et al. to construct a novel SOM : One dimensional ring type growing SOM using asymmetric neighborhood function ( One-D-R-A-G-SOM ) . And it also provides programmability recently . In this paper , the algorithm and result of SOM computing on GPU has been given . The result shows that GPU can make SOM computing much faster than standard CPU . Some methods which detect the abnormal traffics in the network using the information from headers and payloads of IP-packets transmitted in the networks are proposed . In this research , we propose a method of Pareto Learning SOM ( Self Organizing Map ) for IP packet flow analysis in which the occurrence rate is used for SOM computing . The flow of the packets can be visualized on the map and it can be used for detecting the abnormal flows of packets . We present a novel approach for face recognition based on salient singularity descriptors . The automatic feature extraction is performed thanks to a salient point detector , and the singularity information selection is performed by a SOM region-based structuring . The spatial singularity distribution is preserved in order to activate specific neuron maps and the local salient signature stimuli reveals the individual identity . In this paper , we propose a novel receiver structure by combining adaptive RNN ( recurrent neural network ) equalizer with a SOM ( self-organizing map ) detector under serious ISI and nonlinear distortion in QAM system . The performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion . This article describes the hand-held terminal bacon detection system based on Neural Network Pattern recognition . Respectively using computer vision and artificial olfactory , collected the bacon color characteristic value and Plaque as so as the concentration of volatile gases , the collected data through the GPRS to the host computer , then return the result after analysis ; for the determination of the freshness of bacon , inferior fresh and corruption levels of bacon and get a TVB — N content data sequence , the clustering center will be established by the Neural SOM network . Experiments verified that the detection terminal has good detection accuracy and operability . Due to the complexity of the power grid system and the limitations of software and hardware environmental conditions , frequent faults in the power grid are very important for the processing of alarm data . This article analyzed the characteristics of power grid alarm data and proposes an improved fault diagnosis method , Denoising SOM . This method uses the self-organizing map ( SOM ) neural network as a basis , and proposes the idea of denoising the alarm data sample to improve the robustness of the model . SOM , a data mining approach , is a tool for categorizing patterns in n-dimensional observation data by forming a two-dimensional lattice . The bi-directional SOM was applied to the data of daily mean temperature , using flowering ( or full blooming ) dates as teacher signals . We inputted daily mean air temperatures during the thermal accumulation period ( mainly from February to just before flowering ) into multiple input vectors and obtained flowering or full bloom dates into an output vector . In this article , we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map . Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress . We have developed and used the “ CDS document map ” based on neural networks ( Kohonen maps ) http://simbad.u-strasbg.fr/A+A/map.pl In this self-organizing map , documents are gradually clustered by subject themes . The tool is based on keywords associated with the documents . Kohonen ’s Self-organizing Maps . The main purpose of this paper was to understand the ability of Self-Organizing Maps in forecasting the load demand and to train and test via SelfOrganizing Maps method using the selected features . Using data provided by the Global Energy Forecasting Competition ( GEFCom2012 ) , this paper focused on the missing data from the year 2005 and 2006 for the load forecasting . Counter Propagation Network ( CPN ) is presented in this paper to diagnose the fault of power transformer . CPN have both respective advantage of Self-Organizing Map Network and Grossberg Self-Organizing Competitive Network , such as classification flexibility , simple algorithm and fine classification accuracy . In order to improve the classification accuracy , the conception of combination is introduced . This paper presents a three – stage approach to data mining which puts special emphasis on the visualization and interpretability of the results . In the first stage , the input data are represented by a self – organizing map in order to allow visualization and to reduce the amount of data while removing noise , outliers and missing values . Then this preprocessed information is used to identify and display fuzzy clusters of similarity . Thereupon , each document is characterised by comparing the concepts found in it , to those present in the concept space . Applying the characterisations , all documents can be clustered such that semantically similar documents lie close together on a SelfOrganising Map . In this paper we propose a scalable method for time series clustering that replaces the time series point values with some global measures of the characteristics of the time series . These global measures are then clustered using a selforganising map , which performs additional dimension reduction . The proposed approach has been tested using some benchmark time series previously reported for time series clustering , and is shown to yield useful and robust clustering . his paper , a special model in the form of lookup Table is proposed for detecting different abnormal states in mechanical and other industrial systems . The model is generated by repetitive use of a classification procedure that uses a SelfOrganized Map and Ndural Gas algorithm for competitive unsupervised learning . This classification procedure makes a hind of mapping from the measured “ Parameter-Space ” to the preliminary defined “ Operating Mode-Space ” . A selforganized map was designed to learn and detect sleep stages will be described . Initial the input data were preprocessed with Difference Power Spectra ( DPS ( german : DLS ) ) . This paper presents a novel idea of a behavior- based self-organizing map . The self-organizing map ( SOM ) is extended to cover ' objects ' that interact with their envi- ronment . The paper presents a new modification of a self-organizing map topology to increase the speed of feature map convergence . The modification proposed in this paper is useful for real time spectrum sensing applications in which the SOM is used for classification tasks . The invention discloses a spectral clustering method based on landmark point representation , and the implementation steps of the method are as follows : 1 ) inputting data X and clustering number k ; 2 ) generating P landmark points through a Kmeans clustering algorithm or random sampling in the data X ; 3 ) constructing a sparse representation matrix Z of the data X based on the P landmark points ; 4 ) calculating the first k of feature vectors A of ZTZ according to the sparse representation matrix Z ; 5 ) calculating the right singular vector of Z according to the sparse representation matrix to obtain the first k of feature vectors BT of ZZT ; and 6 ) carrying out Kmeans clustering on each line of the feature vectors BT through the Kmeans clustering algorithm , and finally outputting the clustering result . The spectral clustering method has the advantages of being high in spectral clustering algorithm efficiency , low in computation complexity and good in clustering effect . The hunger to manage and process this gigantic complex heterogeneous data , has again followed the age old rule of “ Necessity is the mother of Invention ” , and came up with idea of HadoopMapReduce for the same . The given work uses KMeans clustering algorithm on a benchmark MRI dataset from OASIS database , in order to cluster the data based upon their visual similarity , using WEKA . Until a threshold size it worked out and after that compelled WEKA to prompt an emergency message “ out of memory ” on display . Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well-separated . However , most of kmeans-type clustering algorithms rely on only intra-cluster compactness while overlooking inter-cluster separation . Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated . However , most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation . In this paper , a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation . Empirical analysis is carried out based on the fact of a municipal power supply enterprise in Sichuan province . Keywords-Customer Value , Index System , Energy Saving , KMeans Cluster Using computers to 3,500 in modern Chinese characters commonly used version of Chinese characters and Pinyin input table GB2312 proposed Kmeans clustering analysis based on the classification model . This model through the establishment of Tonal sound symbols and phonetic feature vector , using Kmeans cluster analysis method , will form words by phonetic sound level is divided into six levels , making each one sound within a word and phonetic form more similar degree of and close , and through each grade(cluster of clusters ) in the feature vector model of the size of the cluster results according to degree level phonetic sort . Present a new form of sound words classification model , form , sound character phonogram sound analysis provides a new perspective . The reference methods included clustering of single modalities as well as ensemble clustering of separate and merged data modalities . Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities . To evaluate the e ects of clustering techniques on the performance of PSN routing algorithms , several clustering algorithms are deployed to cluster network nodes . Advanced Graph-based Kmeans ( AGKmeans ) is proposed in this thesis as a clustering algorithm by using the Kmeans clustering concept . This algorithm is appropriate for datasets in which the participants are dynamic and the dataset can be modeled as a graph . On the basis of analyzing the characteristics of infrared images , an im-proved Top-Hat operator is adopted to suppress the noise points in infrared images . And according to traditional Kmeans clustering idea , K-means clustering target detection algorithm based on two-dimensional gradient informationis proposed . Experimental results show that the method has distinctly noise suppression effect , and the target in in-frared images is well detected , which lays a good foundation for the subsequent image processing . Therefore , traditional vector space model(VSM ) and latent semantic analysis(LSA ) are not suitable for modeling them . In this paper , a two stage clustering algorithm based on probabilistic latent semantic analysis(pLSA ) and Kmeans clustering(Kmeans ) is proposed . Besides , this paper also presents the definition of popularity and mechanism of sorting the topics . The slope factor is taken in advantage , and the phenomenon that the smaller classification caused the less optimum solution is avoided . On the basis of traditional KMeans data clustering algorithm , the interference from less local optimum solutions is eliminated by the slope factors . The convergence speed is improved , avoiding convergence to the local optimum solution in early . EdgeCluster [ reference ] : This method uses k - means clustering to cluster the adjacency matrix of G. Its has been shown to perform comparably to the Modularity method , with the added advantage of scaling to graphs which are too large for spectral decomposition . • Specifically , we choose the Product Quantization approach to compress the parameter matrix by first partitioning it horizontally into segments of width . . Then - means clustering is used to cluster the rows in . By only storing the nearest cluster indices in a 8 - bit integer matrix and cluster centers in a single - precision floating number matrix , we can achieve a compression factor . After the encoder is trained , we run it on all instances in the training images and record the obtained features . Then we perform a - means clustering on these features for each semantic category . Each cluster thus encodes the features for a specific style , for example , the asphalt or cobblestone texture for a road . By considering only the best synthesized image , this loss encourages the network to spread its bets and cover the space of images that conform to the input semantic layout . The loss is structurally akin to the k - means clustering objective , which only considers the closest centroid to each datapoint and thus encourages the centroids to spread and cover the dataset . We further build on this idea and formulate a loss that considers a virtual collection of up to k c images . In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network . In contrast we employ a k - means like clustering objective which simplifies the optimization significantly and does not require a recurrent procedure . Furthermore , we investigate both unsupervised and supervised learning . : Update weights . 13 : Update drifted cluster centers using Kmeans ++ every M iterations . 14 The center or its nearest neighbour are selected as exemplars . Perhaps , Kmeans and Kmedoids are the most typical methods ( Kmedoids is a variant of Kmeans ) . Both methods employ an EM - like algorithm . The key feature of such data is the extreme sparsity , which renders conventional anonymization techniques not working well in retaining enough utility . We propose a semantic similarity based clustering approach to address the issue . We measure the semantic similarity between two vocabularies by a weighted bipartite matching and present a greedy algorithm to cluster vocabularies by the semantic similarities .
K-means algorithm is a common and effective clustering algorithm based on partition . To solve the problem of sensitivity of initial cluster centers , the most frequently used method is searching optimal initial cluster centers by hierarchically initializing .
In many applications , attribute and relationship data areavailable , carrying complementary information about real world entities . In such cases , a joint analysis of both types of data can yield more accurate results than classical clustering algorithms that either use only attribute data or only relationship ( graph ) data . The Connected k-Center ( CkC ) has been proposed as the first joint cluster analysis model to discover k clusters which are cohesive on both attribute and relationship data .
Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets . Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation , in comparison with four other popular clustering algorithms . ConclusionFor the first time partial mixture model is successfully extended to time-course data analysis . The robustness of our partial regression clustering algorithm proves the suitability of the combination of both partial mixture model and minimum distance estimator in this field . We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge , but also presents interesting new hypotheses during interpretation of clustering results .
Traditional collaborative filtering algorithm does not take into account the user ’s interest factors , at the same time , it has the problem of sparse data , poor scalability and so on , which directly affects the quality of the recommendation . This paper proposes a collaborative filtering recommendation algorithm based on user interest clustering . Firstly , according to the user ’s existing score , a method is used to compute the interest degree of the user .
Through clustering the two phases of dense data subset and the whole data set , the clustering effect of extreme sparse data sets are improved . At the same time , because of fuzziness of graph clustering , this thesis retains a certain diversity in the process of user interest clustering , so as to avoid convergence too fast when cold start . This method is verified through the real social network data , and the experimental results show that this algorithm can effectively solve the problems such as data sparseness and cold start phenomenon .
The theory is applied to the address block location problem . A multiple locator algorithm employing a machine-printed address block locator , a handwritten address block locator , and a clustering-based locator is created and tested on a database of 1,100 mail piece images , in two separate experiments . The experimental results show that combining location results using the theory provides location performance that excels any individual locator performance , and therefore is a viable approach to document object location problems .
CSO is a latest meta-heuristic algorithm based on the behavior of cats . In this paper , we present a clustering-based test case prioritization technique . The method consists of clustering the test cases based on fault detected by test cases .
Abstract Due to high dimensionality and multiple variables , unsupervised classification of 12-lead ECG signals involves challenges and difficulties . In order to automatically discover unknown physiological features from raw multivariate signals and detect abnormal cardiac activities of a subject , we proposed an unsupervised classification scheme of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering . After filtering and segmentation , each ECG sample is converted into a wavelet tensor by the Discrete Wavelet Packet Transform ( DWPT ) .
Such an organization presents a basic conundrum : Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer . It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem . However , there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity , a global property unrelated to local clustering .
We present measures to quantify the shadowing effect , and with Faceted Clustering we introduce an algorithm that optimizes the identified principles . The key idea of Faceted Clustering is a dynamic , user-controlled reorganization of a clustering , similar to a faceted navigation system . We report on evaluations using the AMBIENT corpus and demonstrate the potential of our approach by a comparison with two well-known clustering search engines .
But it adds complexity for database querying and updating . We improve it and propose CET ( Clustering Extension Table ) . There are several tables in CET , including BT ( Basic Table ) , CT ( Clustering Table ) , CMT ( Metadata Table for CT ) , ET ( Extension Table ) and EMT ( Metadata Table for ET ) .
So , using Hill Climbing Automatic Clustering to function identify about movement variance from each phase formation cluster and analyze pattern so that cluster total will be formed in automatic . The use of text mining , clustering by CLHM and Hill Climbing Automatic Clustering process to make easy user because get result clustering precisely and automaticaly .
The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances . Once this embedding has been produced , then the aforementioned tasks become straight - forward : face verification simply involves thresholding the distance between the two embeddings ; recognition becomes a k - NN classification problem ; and clustering can be achieved using off - the - shelf techniques such as k - means or agglomerative clustering . Previous face recognition approaches based on deep networks use a classification layer trained over a set of known face identities and then take an intermediate bottleneck layer as a representation used to generalize recognition beyond the set of identities used in training .
document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples .
In the rest of the paper , we begin by explaining the difference between semantic clustering and intermediate representation learning ( s : related ) , which separates our method from the majority of work in unsupervised deep learning . We then describe the theoretical foundations of IIC in statistical learning ( s : method ) , demonstrating that maximising mutual information between pairs of samples under a bottleneck is a principled clustering objective which is equivalent to distilling their shared abstract content ( co - clustering ) . We propose that for static images , an easy way to generate pairs with shared abstract content from unlabelled data is to take each image and its random transformation , or each patch and a neighbour .
Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion . Agglomerative clustering and partially ordered sets of HOG features have been used to group images , and exemplars define a group as a set of random transformations applied to a single image . Note the latter does not scale easily , in particular to image segmentation where a single image would call for 40k classes .
This is corrected by the conditional entropy component , which encourages deterministic one - hot predictions . For example , even for the degenerate case of identical pairs , the IIC objective encourages a deterministic clustering function ( i.e. is a one - hot vector ) as this results in null conditional entropy . Secondly , the objective of IIC is to find what is common between two data points that share redundancy , such as different images of the same object , explicitly encouraging distillation of the common part while ignoring the rest , i.e. instance details specific to one of the samples .
[ 33 ] unrolls the iterations forming the sparse codes and optimizes end - to - end the involved parameters using a clustering objective as loss function [ 33 ] . The proposed framework is further augmented by clustering objectives applied to intermediate representations , which act as feature regularization within the unrolled optimization . They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts . Our method differs in that we use convolutional neural networks rather than unrolling a sparse coding optimization . In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network . In contrast we employ a k - means like clustering objective which simplifies the optimization significantly and does not require a recurrent procedure .
For spatial clustering the dimension of the matrix µ is K × C. ( C ) Channel Co - Clustering : This regularizer groups the channels of different samples directly , thus co - clustering samples and filters . We expect this type of regularization to model re - occurring Algorithm 1 :
Most of the recent approaches use Convolutional Neural Networks ( CNNs ) to detect body parts and relationships between them in an end - to - end manner , then use assignment algorithms to form individual skeletons . Pischulin et al . used deep features for joint prediction of part locations and relations between them , then performed correlation clustering . Even though does n’t use person detections , it is very slow due to proposed clustering algorithm and processing time is in the order of hours .
In contrast , our novel formulation relies on fully connected model to deal with unknown number of people per image and body parts per person . The Minimum Cost Multicut Problem , known in machine learning as correlation clustering , has been used in computer vision for image segmentation but has not been used before in the context of pose estimation . It is known to be NP - hard .
This strategy can achieve a joint maximum purity for all the split - nodes when training a random forest . Image super - resolution can be performed based on clustering / classification , according to the recent emerging clustering - regression stream [ reference ][ reference ][ reference ] , and the JMPF scheme can achieve remarkable performance on both the classification and regression tasks . Therefore , JMPF is applied to single - image super - resolution in this paper .
Mean shift clustering vs ground truth clustering Since the output of our network needs to be clustered into discrete instances , the clustering method can potentially influence the accuracy of the overall instance segmentation . In this experiment , we measure this influence by clustering with our adapted mean shift clustering algorithm versus thresholding around the mean embeddings over the ground truth instance masks , as explained in section [ reference ] ) . subsection : Results and discussion
The objective of the paper is to give the user internal information contained in the data by using only the binary class-information data . A self organizing map ( SOM ) is used as the main tool for this purpose . Our method is to tune the weight of the elements of the data so that the data of the same category tend to be mapped in the near points on the SOM , and the separation of different categories can be carried out successfully .
The purpose of the proposed system is to create autonomous systems that can learn independently and cooperate to provide a better decision of the phoneme classification . The basic SOM variant is a hybrid model of SOM and Genetic Algorithm ( GA ) using a growing incremental technique to adapt the map structure and extra information in the map units to optimise the map codebooks . The hierarchical evolutionary learning algorithm classifies data according to tow hierarchical levels ensuring the representation of the hierarchical relations of the data .
In this paper , we propose a two-level self-organizing map ( SOM ) that automatically responds to the users ' questions on Internet , and helps them to find their answer for themselves by browsing the map hierarchically . The method consists of keyword clustering SOM that reduces a variable length question to a normalized vector , and document classification SOM that classifies the question into an answer class . The final map is also used for browsing .
In this paper , we propose an assistant system which responds to the users questions automatically and helps them browse the Hanmail Net FAQ ( Frequently Asked Question ) conceptually . This system uses two-level self-organizing map ( SOM ) : the keyword clustering SOM and document classification SOM . The keyword clustering SOM reduces a variable length question to a normalized vector and the document classification SOM classifies the question into an answer class .
In this paper , a novel self-organizing map ( SOM ) named “ One-D-R-A-G-SOM ” is proposed . It is a kind of one dimensional ring type growing SOM using asymmetric neighborhood function . As the topology of one dimensional ring type feature map is more suitable to increase or decrease the number of units , and the disorder of the map is available to be solved by the asymmetric neighborhood function , the proposed model gives priority of learning performance to the conventional two dimensional growing SOM .
An asymmetric neighborhood function was proposed by Aoki and Aoyagi to instead of symmetric neighborhood function in conventional Kohonen 's self-organizing map ( SOM ) to avoid topological twist of the order of units during training process . Meanwhile , a one dimensional ring type growing SOM was proposed by Ohta and Saito to reduce the unnecessary increasing of units of conventional 2-D growing SOM . In this paper , we adopt the asymmetric neighborhood to a parameterless growing SOM ( PL-G-SOM ) proposed by Kuremoto et al. to construct a novel SOM : One dimensional ring type growing SOM using asymmetric neighborhood function ( One-D-R-A-G-SOM ) .
And it also provides programmability recently . In this paper , the algorithm and result of SOM computing on GPU has been given . The result shows that GPU can make SOM computing much faster than standard CPU .
Some methods which detect the abnormal traffics in the network using the information from headers and payloads of IP-packets transmitted in the networks are proposed . In this research , we propose a method of Pareto Learning SOM ( Self Organizing Map ) for IP packet flow analysis in which the occurrence rate is used for SOM computing . The flow of the packets can be visualized on the map and it can be used for detecting the abnormal flows of packets .
We present a novel approach for face recognition based on salient singularity descriptors . The automatic feature extraction is performed thanks to a salient point detector , and the singularity information selection is performed by a SOM region-based structuring . The spatial singularity distribution is preserved in order to activate specific neuron maps and the local salient signature stimuli reveals the individual identity .
In this paper , we propose a novel receiver structure by combining adaptive RNN ( recurrent neural network ) equalizer with a SOM ( self-organizing map ) detector under serious ISI and nonlinear distortion in QAM system . The performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion .
This article describes the hand-held terminal bacon detection system based on Neural Network Pattern recognition . Respectively using computer vision and artificial olfactory , collected the bacon color characteristic value and Plaque as so as the concentration of volatile gases , the collected data through the GPRS to the host computer , then return the result after analysis ; for the determination of the freshness of bacon , inferior fresh and corruption levels of bacon and get a TVB — N content data sequence , the clustering center will be established by the Neural SOM network . Experiments verified that the detection terminal has good detection accuracy and operability .
Due to the complexity of the power grid system and the limitations of software and hardware environmental conditions , frequent faults in the power grid are very important for the processing of alarm data . This article analyzed the characteristics of power grid alarm data and proposes an improved fault diagnosis method , Denoising SOM . This method uses the self-organizing map ( SOM ) neural network as a basis , and proposes the idea of denoising the alarm data sample to improve the robustness of the model .
SOM , a data mining approach , is a tool for categorizing patterns in n-dimensional observation data by forming a two-dimensional lattice . The bi-directional SOM was applied to the data of daily mean temperature , using flowering ( or full blooming ) dates as teacher signals . We inputted daily mean air temperatures during the thermal accumulation period ( mainly from February to just before flowering ) into multiple input vectors and obtained flowering or full bloom dates into an output vector .
In this article , we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map . Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress .
We have developed and used the “ CDS document map ” based on neural networks ( Kohonen maps ) http://simbad.u-strasbg.fr/A+A/map.pl In this self-organizing map , documents are gradually clustered by subject themes . The tool is based on keywords associated with the documents .
Kohonen ’s Self-organizing Maps . The main purpose of this paper was to understand the ability of Self-Organizing Maps in forecasting the load demand and to train and test via SelfOrganizing Maps method using the selected features . Using data provided by the Global Energy Forecasting Competition ( GEFCom2012 ) , this paper focused on the missing data from the year 2005 and 2006 for the load forecasting .
Counter Propagation Network ( CPN ) is presented in this paper to diagnose the fault of power transformer . CPN have both respective advantage of Self-Organizing Map Network and Grossberg Self-Organizing Competitive Network , such as classification flexibility , simple algorithm and fine classification accuracy . In order to improve the classification accuracy , the conception of combination is introduced .
This paper presents a three – stage approach to data mining which puts special emphasis on the visualization and interpretability of the results . In the first stage , the input data are represented by a self – organizing map in order to allow visualization and to reduce the amount of data while removing noise , outliers and missing values . Then this preprocessed information is used to identify and display fuzzy clusters of similarity .
Thereupon , each document is characterised by comparing the concepts found in it , to those present in the concept space . Applying the characterisations , all documents can be clustered such that semantically similar documents lie close together on a SelfOrganising Map .
In this paper we propose a scalable method for time series clustering that replaces the time series point values with some global measures of the characteristics of the time series . These global measures are then clustered using a selforganising map , which performs additional dimension reduction . The proposed approach has been tested using some benchmark time series previously reported for time series clustering , and is shown to yield useful and robust clustering .
his paper , a special model in the form of lookup Table is proposed for detecting different abnormal states in mechanical and other industrial systems . The model is generated by repetitive use of a classification procedure that uses a SelfOrganized Map and Ndural Gas algorithm for competitive unsupervised learning . This classification procedure makes a hind of mapping from the measured “ Parameter-Space ” to the preliminary defined “ Operating Mode-Space ” .
A selforganized map was designed to learn and detect sleep stages will be described . Initial the input data were preprocessed with Difference Power Spectra ( DPS ( german : DLS ) ) .
This paper presents a novel idea of a behavior- based self-organizing map . The self-organizing map ( SOM ) is extended to cover ' objects ' that interact with their envi- ronment .
The paper presents a new modification of a self-organizing map topology to increase the speed of feature map convergence . The modification proposed in this paper is useful for real time spectrum sensing applications in which the SOM is used for classification tasks .
The invention discloses a spectral clustering method based on landmark point representation , and the implementation steps of the method are as follows : 1 ) inputting data X and clustering number k ; 2 ) generating P landmark points through a Kmeans clustering algorithm or random sampling in the data X ; 3 ) constructing a sparse representation matrix Z of the data X based on the P landmark points ; 4 ) calculating the first k of feature vectors A of ZTZ according to the sparse representation matrix Z ; 5 ) calculating the right singular vector of Z according to the sparse representation matrix to obtain the first k of feature vectors BT of ZZT ; and 6 ) carrying out Kmeans clustering on each line of the feature vectors BT through the Kmeans clustering algorithm , and finally outputting the clustering result . The spectral clustering method has the advantages of being high in spectral clustering algorithm efficiency , low in computation complexity and good in clustering effect .
The hunger to manage and process this gigantic complex heterogeneous data , has again followed the age old rule of “ Necessity is the mother of Invention ” , and came up with idea of HadoopMapReduce for the same . The given work uses KMeans clustering algorithm on a benchmark MRI dataset from OASIS database , in order to cluster the data based upon their visual similarity , using WEKA . Until a threshold size it worked out and after that compelled WEKA to prompt an emergency message “ out of memory ” on display .
Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well-separated . However , most of kmeans-type clustering algorithms rely on only intra-cluster compactness while overlooking inter-cluster separation .
Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated . However , most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation . In this paper , a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation .
Empirical analysis is carried out based on the fact of a municipal power supply enterprise in Sichuan province . Keywords-Customer Value , Index System , Energy Saving , KMeans Cluster
Using computers to 3,500 in modern Chinese characters commonly used version of Chinese characters and Pinyin input table GB2312 proposed Kmeans clustering analysis based on the classification model . This model through the establishment of Tonal sound symbols and phonetic feature vector , using Kmeans cluster analysis method , will form words by phonetic sound level is divided into six levels , making each one sound within a word and phonetic form more similar degree of and close , and through each grade(cluster of clusters ) in the feature vector model of the size of the cluster results according to degree level phonetic sort . Present a new form of sound words classification model , form , sound character phonogram sound analysis provides a new perspective .
The reference methods included clustering of single modalities as well as ensemble clustering of separate and merged data modalities . Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities .
To evaluate the e ects of clustering techniques on the performance of PSN routing algorithms , several clustering algorithms are deployed to cluster network nodes . Advanced Graph-based Kmeans ( AGKmeans ) is proposed in this thesis as a clustering algorithm by using the Kmeans clustering concept . This algorithm is appropriate for datasets in which the participants are dynamic and the dataset can be modeled as a graph .
On the basis of analyzing the characteristics of infrared images , an im-proved Top-Hat operator is adopted to suppress the noise points in infrared images . And according to traditional Kmeans clustering idea , K-means clustering target detection algorithm based on two-dimensional gradient informationis proposed . Experimental results show that the method has distinctly noise suppression effect , and the target in in-frared images is well detected , which lays a good foundation for the subsequent image processing .
Therefore , traditional vector space model(VSM ) and latent semantic analysis(LSA ) are not suitable for modeling them . In this paper , a two stage clustering algorithm based on probabilistic latent semantic analysis(pLSA ) and Kmeans clustering(Kmeans ) is proposed . Besides , this paper also presents the definition of popularity and mechanism of sorting the topics .
The slope factor is taken in advantage , and the phenomenon that the smaller classification caused the less optimum solution is avoided . On the basis of traditional KMeans data clustering algorithm , the interference from less local optimum solutions is eliminated by the slope factors . The convergence speed is improved , avoiding convergence to the local optimum solution in early .
EdgeCluster [ reference ] : This method uses k - means clustering to cluster the adjacency matrix of G. Its has been shown to perform comparably to the Modularity method , with the added advantage of scaling to graphs which are too large for spectral decomposition . •
Specifically , we choose the Product Quantization approach to compress the parameter matrix by first partitioning it horizontally into segments of width . . Then - means clustering is used to cluster the rows in . By only storing the nearest cluster indices in a 8 - bit integer matrix and cluster centers in a single - precision floating number matrix , we can achieve a compression factor .
After the encoder is trained , we run it on all instances in the training images and record the obtained features . Then we perform a - means clustering on these features for each semantic category . Each cluster thus encodes the features for a specific style , for example , the asphalt or cobblestone texture for a road .
By considering only the best synthesized image , this loss encourages the network to spread its bets and cover the space of images that conform to the input semantic layout . The loss is structurally akin to the k - means clustering objective , which only considers the closest centroid to each datapoint and thus encourages the centroids to spread and cover the dataset . We further build on this idea and formulate a loss that considers a virtual collection of up to k c images .
In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network . In contrast we employ a k - means like clustering objective which simplifies the optimization significantly and does not require a recurrent procedure . Furthermore , we investigate both unsupervised and supervised learning .
: Update weights . 13 : Update drifted cluster centers using Kmeans ++ every M iterations . 14
The center or its nearest neighbour are selected as exemplars . Perhaps , Kmeans and Kmedoids are the most typical methods ( Kmedoids is a variant of Kmeans ) . Both methods employ an EM - like algorithm .
The key feature of such data is the extreme sparsity , which renders conventional anonymization techniques not working well in retaining enough utility . We propose a semantic similarity based clustering approach to address the issue . We measure the semantic similarity between two vocabularies by a weighted bipartite matching and present a greedy algorithm to cluster vocabularies by the semantic similarities .
Research on a method of self-adaptation of the number of clusters for hierarchical initialization clustering Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters Partial mixture model for tight clustering of gene expression time-course Research on recommendation system based on interest clustering User Clustering Topic Recommendation Algorithm Based on Two Phase in the Social Network Theory of Document Object Locator Combination A New Cluster-Based Test Case Prioritization Using Cat Swarm Optimization Technique Unsupervised classification of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks Search result presentation based on faceted clustering CET: Clustering Extension Table research in multi-tenant database for SaaS applications WEB MINING UNTUK PENCARIAN DOKUMEN BAHASA INGGRIS MENGGUNAKAN HILL CLIMBING AUTOMATIC CLUSTERING FaceNet: A unified embedding for face recognition and clustering Invariant Information Clustering for Unsupervised Image Classification and Segmentation Learning Deep Parsimonious Representations MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation Joint maximum purity forest with application to image super-resolution Semantic Instance Segmentation with a Discriminative Loss Function Weight tuning and pattern classification by self organizing map using genetic algorithm Hierarchical Evolutionary Learning of Growing Incremental Self-Organizing Maps for Phoneme Classification Application of Self-Organizing Maps to Classification and Browsing of FAQ E-mails Automatic Response and Conceptual Browsing of Internet FAQs Using Self-Organizing Maps One-D-R-A-G-SOM and its Application to a Hand Shape Instruction Learning System One dimensional ring type growing SOM with asymmetric neighborhood function and its application to a hand shape instruction learning system Self-Organizing Maps computing on Graphic Process Unit Analysis of Packet Traffics and Detection of Abnormal Traffics Using Pareto Learning Self Organizing Maps Facial biometry by stimulating salient singularity masks Combining RNN equalizer with SOM detector Research and realization of hand-held mobile bacon detection based on Neural Network Pattern recognition Denoising SOM: An Improved Fault Diagnosis Method for Quantum Power Communication Network A simpler way to predict flowering and full bloom dates of cherry blossoms by self-organizing maps Paths of Wellbeing on Self-Organizing Maps Comparison of Two ``Document Similarity Search Engines'' Medium Term Load Forecasting Using Statistical Feature Self Organizing Maps (SOM) Application of Counter Propagation Network in Fault Diagnosis of Power Transformer Mining clusters and corresponding interpretable descriptions - a three-stage approach Information retrieval systems using an associative conceptual space A Scalable Method for Time Series Clustering Classification-based behavior model for detection of abnormal states in systems Detection of Sleep with New Preprocessing Methods for EEG Analyzing volut ionary Training of Behavior-Base rganizing Ma A new self-organizing map topology for real-time spectrum sensing and fast convergence Spectral clustering method based on landmark point representation An Intelligent Distributed K-means Algorithm over Cloudera /Hadoop Extending Kmeans-Type Algorithms by Integrating Intra-cluster Compactness and Inter-cluster Separation Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation Power Customer Value Assessment Research Based on K-Means Method Application of Kmeans Clustering Analysis in Chinese Pronunciation Degree Complementary ensemble clustering of biomedical data A Simulation Performance Study of PSN Algorithms in Practical Use Cases Infrared Target Detection Based on K-means Clustering Microblog Hot Topics Discovery Method Based on Probabilistic Topic Model Fault Diagnosis and Optimal Data Clustering Based on K-Means with Slope Factor DeepWalk: online learning of social representations HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Photographic Image Synthesis with Cascaded Refinement Networks Learning Deep Parsimonious Representations 10, 000+ Times Accelerated Robust Subset Selection (ARSS) Enforcing Vocabulary k-Anonymity by Semantic Similarity Based Clustering
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KDD CIKM 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 IEEE/CVF International Conference on Computer Vision (ICCV) NIPS ECCV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Electronic Imaging ArXiv International Conference on Evolutionary Computation PRICAI Workshops SNPD ESANN ICONIP (2) AVSS ESANN Fuzzy Days EUROCON KDD '14 2015 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) NIPS AAAI 2015 ICDM
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