The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached. The present invention resolves problems that how to realize clustering of high dimensional and nonlinear data space and that the prior art consumes a larger memory or need longer running time.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A clustering method based on iterations of neural networks comprising the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to a number of clusters, each sample representing one cluster, and training the extreme learning machine to obtain an initial output weights between hidden-layer and output-layer; step 3, using current extreme learning machine to cluster samples; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from previous step; and step 6, outputting clustering result if termination condition is met, otherwise going back to step 3. 2. A clustering method based on iterations of neural networks of claim 1, wherein step 1 comprises two substeps: substep 1.1, setting a number of hidden-layer neurons L, an activation function of hidden-layer g(θ), and a regularization factor γ of the extreme learning machine, wherein the number of hidden-layer neurons L is greater than a feature dimension d of sample, the activation function of hidden-layer g(θ) usually adopts Sigmoid function, and value range of the regularization factor γ is generally [10−5, 1010]; and substep 1.2, randomly initializing weights between input-layer and hidden-layer W and a bias b of the extreme learning machine, wherein the weights between input-layer and hidden-layer W is a random matrix with d rows and L columns whose random range is [−1,1], and the bias b is a random matrix with 1 row and L columns whose random range is [−1,1], one of columns of the bias corresponding to a bias of a hidden-layer neuron. 3. A clustering method based on iterations of neural networks of claim 1, wherein step 2 comprises two substeps: substep 2.1, randomly choosing samples of which number is equal to the number of clusters, that is, choosing K samples, each sample representing one cluster, so as to form an initial exemplar set and corresponding target output to train the extreme learning machine; and substep 2.2, calculating an output of the hidden-layer neurons H(0) and a output weights between hidden-layer and output-layer β(0) to obtain an initial model of the extreme learning machine through Eq. (2), Eq. (3) and Eq. (4): H ( 0 ) = g  ( W · E ( 0 ) + B  ( 0 ) ) ( 2 ) if   K > L , β ( 0 ) = ( H ( 0 ) T  H ( 0 ) + I L γ ) †  H ( 0 ) T  T ( 0 ) ( 3 ) else   β ( 0 ) = H ( 0 ) T  ( H ( 0 )  H ( 0 ) T + I K γ ) †  T ( 0 ) ( 4 ) wherein B(0) is a row-expansive matrix of the bias b of which number of rows is equal to that of E(0), which is a matrix with K rows and L columns, K being the number of clusters, and L being the number of hidden-layer neurons, H(0)T represents a transpose of the matrix H(0), (•)† represents a generalized inverse matrix, IL is an identity matrix of L dimension, and IK is an identity matrix of K dimension. 4. A clustering method based on iterations of neural networks of claim 1, wherein step 3 comprises two substeps: Substep 3.1, calculating an output value of the sample xi on an output-layer neuron oi by using parameters of the extreme learning machine currently used through Eq. (5) and Eq. (6), wherein the weights between input-layer and hidden-layer W, the bias b, the activation function of hidden-layer g(θ), the number of hidden-layer neurons L and the regularization factor γ are changeless during iterations, while the output weights between hidden-layer and output-layer β continues to change as iterations progress, Eq. (5) and Eq. (6) being as follows: hi=g(Wxi+b) (5) oi=hiβ(m-1) (6) wherein hi represents an output value of the sample xi on the hidden-layer and is a matrix with 1 row and L columns, oi represents the output value of the sample x1 on the output-layer and is a matrix with 1 row and K columns, and β(m-1) represents a output weights between hidden-layer and output-layer obtained in the previous iteration; and substep 3.2, clustering the samples according to the output value of the samples on the output-layer neurons, wherein assigning rule is that a sample should be assigned to a cluster corresponding to the neuron with the highest output value of the output-layer neurons on basis of which the number of the output-layer neurons is equal to the number of clusters. 5. A clustering method based on iterations of neural networks of claim 1, wherein step 4 comprises two substeps: substep 4.1, calculating each cluster mean MEAN according to clustering result of step 3; substep 4.2, calculating distances between each sample of the cluster and a cluster center in each cluster, and choosing ρ samples which are the nearest to the cluster center to form an exemplar set of current iteration E(m), wherein if the number of samples of a cluster is less than the number of exemplars ρ required to be selected from the cluster, all samples of the cluster are selected to be exemplars for the cluster; and if a cluster doesn't contain any sample, a sample as a exemplar for the cluster should be randomly selected from all samples with exception of exemplar sets for all the other clusters. 6. A clustering method based on iterations of neural networks of claim 1, wherein step 5 comprises two substeps: substep 5.1, constructing target functions for exemplar sets obtained from step 4; and substep 5.2, calculating an output of the hidden-layer neurons H(m) and a output weights between hidden-layer and output-layer β(m) of the extreme learning machine after being retrained through Eq. (7), Eq. (8) and Eq. (9): H ( m ) = g  ( W · E ( m ) + B ( m ) ) ( 7 ) if   K × ρ > L , β ( m ) = ( H ( m ) T  H ( m ) + I L γ ) †  H ( m ) T  T ( m ) ( 8 ) else   β ( m ) = H ( m ) T  ( H ( m )  H ( m ) T + I K × ρ γ ) †  T ( m ) ( 9 ) wherein B(m) is a row-expansive matrix of the bias b of which number of rows is equal to that of E(m), which is a matrix with K×ρ rows and L columns, K being the number of clusters, ρ being the number of exemplars of each cluster, and L being the number of hidden-layer neurons, H(m)T represents a transpose of the matrix H(m), (•)† represents the generalized inverse matrix, IL is the identity matrix of L dimension, and IK×ρ is an identity matrix of K×ρ dimension.