Classifying large data sets using SVMs with hierarchical clusters

Hwanjo Yu, Jiong Yang, Jiawei Han

Research output: Contribution to conferencePaper

Abstract

Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, Clustering-Based SVM (CB-SVM), which is specifically designed for handling very large data sets. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CB-SVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CB-SVM is highly scalable for very large data sets while also generating high classification accuracy.

Original languageEnglish (US)
Pages306-315
Number of pages10
DOIs
StatePublished - Dec 1 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003

Other

Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
CountryUnited States
CityWashington, DC
Period8/24/038/27/03

Keywords

  • Hierarchical cluster
  • Support vector machines

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Yu, H., Yang, J., & Han, J. (2003). Classifying large data sets using SVMs with hierarchical clusters. 306-315. Paper presented at 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, Washington, DC, United States. https://doi.org/10.1145/956750.956786