Predictive analytic models are hierarchically built based on a training dataset, which includes pairs of input data and output data. First, the input data and the output data are preprocessed. A hierarchical clustering process is performed on the dataset. The hierarchical clustering process comprises level-1 input and output data clustering, level-2 input and output data clustering, and so on, up to level-K input and output data clustering, where K is an integer greater than one. A hierarchical model building process is performed. The hierarchical model building process comprises level-1 model building over level-1 clustered input and output data, level-2 model building over level-2 clustered input and output data, and so on, up to level-K model building over level-K clustered input and output data. At least one level-K predictive model is generated as the resulting built model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method which hierarchically builds a plurality of predictive analytic models based on a training dataset, the method comprising: preprocessing the training dataset that includes an input dataset and an output dataset, both of which comprise a plurality of features; hierarchically clustering the training dataset, wherein the hierarchical clustering comprises K levels of clustering of the input dataset and the output dataset to produce K levels of clustered input and output data, wherein K is an integer greater than one; hierarchically building the plurality of predictive analytic models, which further comprises training K levels of predictive models over the K levels of clustered input and output data, respectively; and generating at least a level-K predictive model as an output. 2. The method of claim 1, wherein the training dataset is a high-volume dataset that includes a number of data records greater than a volume threshold. 3. The method of claim 2, further comprising: hierarchically clustering data records in the training dataset to produce hierarchically clustered data records; and hierarchically training the K levels of predictive models using the hierarchically clustered data records. 4. The method of claim 1, wherein the training dataset is a high-dimensional dataset that includes a number of features greater than a feature threshold. 5. The method of claim 4, wherein each of the K levels of clustering is a k-means feature clustering, and each of the K levels of predictive models is an artificial neural network. 6. The method of claim 4, further comprising: hierarchically clustering data features in the training dataset to produce hierarchically clustered data features; and hierarchically training the K levels of predictive models using the hierarchically clustered data features. 7. The method of claim 1, further comprising: computing a set of local optimal predictive models at a lower level of a model hierarchy; and propagating the set of local optimal predictive models to higher levels of the model hierarchy. 8. The method of claim 7, wherein computing the set of local optimal predictive models further comprises: computing a set of local optimal solutions using a TRUST-TECH method, which computes a set of stable equilibrium points (SEPs) of a dynamical system, wherein each SEP is a set of local optimal model parameters that corresponds to a local optimal predictive model. 9. The method of claim 1, further comprising: building a predictive model at a given level of 1 model hierarchy using the clustered input and output data from levels not exceeding the given level and a built predictive model at one level lower than the given level. 10. A system which hierarchically builds a plurality of predictive analytic models based on a training dataset, the system comprising: one or more processors; and a memory, the memory containing instructions executable by the one or more processors, the one or more processors operable to: preprocess the training dataset that includes an input dataset and an output dataset, both of which comprise a plurality of features; hierarchically cluster the training dataset by clustering K levels of the input dataset and the output dataset to produce K levels of clustered input and output data, wherein K is an integer greater than one; hierarchically build the plurality of predictive analytic models by training K levels of predictive models over the K levels of clustered input and output data, respectively; and generate at least a level-K predictive model as an output. 11. The system of claim 10, wherein the training dataset is a high-volume dataset that includes a number of data records greater than a volume threshold. 12. The system of claim 11, wherein the one or more processors is further operable to: hierarchically clustering data records in the training dataset to produce hierarchically clustered data records; and hierarchically training the K levels of predictive models using the hierarchically clustered data records. 13. The system of claim 10, wherein the training dataset is a high-dimensional dataset that includes a number of features greater than a feature threshold. 14. The system of claim 13, wherein each of the K levels of clustering is a k-means feature clustering, and each of the K levels of predictive models is an artificial neural network. 15. The system of claim 13, wherein the one or more processors is further operable to: hierarchically clustering data features in the training dataset to produce hierarchically clustered data features; and hierarchically training the K levels of predictive models using the hierarchically clustered data features. 16. The system of claim 10, wherein the one or more processors is further operable to: computing a set of local optimal predictive models at a lower level of a model hierarchy; and propagating the set of local optimal predictive models to higher levels of the model hierarchy. 17. The system of claim 16, wherein the one or more processors is further operable to: computing a set of local optimal solutions using a TRUST-TECH method, which computes a set of stable equilibrium points (SEPs) of a dynamical system, wherein each SEP is a set of local optimal model parameters that corresponds to a local optimal predictive model. 18. The system of claim 10, wherein the one or more processors is further operable to: building a predictive model at a given level of 1 model hierarchy using the clustered input and output data from levels not exceeding the given level and a built predictive model at one level lower than the given level. 19. A non-transitory computer readable storage medium including instructions that, when executed by a computing system, cause the computing system to perform a method which hierarchically builds a plurality of predictive analytic models based on a training dataset, the method comprising: preprocessing the training dataset that includes an input dataset and an output dataset, both of which comprise a plurality of features; hierarchically clustering the training dataset, wherein the hierarchical clustering comprises K levels of clustering of the input dataset and the output dataset to produce K levels of clustered input and output data, wherein K is an integer greater than one; hierarchically building the plurality of predictive analytic models, which further comprises training K levels of predictive models over the K levels of clustered input and output data, respectively; and generating at least a level-K predictive model as an output. 20. The non-transitory computer readable storage medium of claim 19, wherein the training dataset is a high-volume dataset that includes a number of data records greater than a volume threshold, or a high-dimensional dataset that includes a number of features greater than a feature threshold.