Classifying large data sets using SVMs with hierarchical clusters

Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey 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 learni...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Hwanjo Yu, Jiong Yang, Jiawei Han
• Keywords: Data Mining, Entire Data Set, KDD 2003, Large Data Sets, Real Data Sets