# Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing

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## Abstract

Support vector machines (SVMs) have been promising methods for classification and regression analysis due to their solid mathematical foundations, which include two desirable properties: margin maximization and nonlinear classification using kernels. However, despite these prominent properties, SVMs are usually not chosen for large-scale data mining problems because their training complexity is highly dependent on the data set size. Unlike traditional pattern recognition and machine learning, real-world data mining applications often involve huge numbers of data records. Thus it is too expensive to perform multiple scans on the entire data set, and it is also infeasible to put the data set in memory. This paper presents a method, *Clustering-Based SVM (CB-SVM)*, that maximizes the SVM performance for very large data sets given a limited amount of resource, e.g., memory. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples. These samples carry statistical summaries of the data and maximize the benefit of learning. Our analyses show that the training complexity of CB-SVM is quadratically dependent on the number of support vectors, which is usually much less than that of the entire data set. Our experiments on synthetic and real-world data sets show that CB-SVM is highly scalable for very large data sets and very accurate in terms of classification.

## Keywords

Training Time Cluster Feature Training Data Point Nonlinear Kernel Nonlinear Classification## Notes

### Acknowledgments

The work was supported in part by National Science Foundation under grants No. IIS-02-09199/IIS-03-08215 and an IBM Faculty Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

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