Clustering Via Local Regression

Authors:
Jun Sun;Zhiyong Shen;Hui Li;Yidong Shen
Affiliations:
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University, Chinese Academy of Sciences, Beijing, China 100049;State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University, Chinese Academy of Sciences, Beijing, China 100049;Graduate University, Chinese Academy of Sciences, Beijing, China 100049;State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

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Abstract

This paper deals with the local learning approach for clustering, which is based on the idea that in a good clustering, the cluster label of each data point can be well predicted based on its neighbors and their cluster labels. We propose a novel local learning based clustering algorithm using kernel regression as the local label predictor. Although sum of absolute error is used instead of sum of squared error, we still obtain an algorithm that clusters the data by exploiting the eigen-structure of a sparse matrix. Experimental results on many data sets demonstrate the effectiveness and potential of the proposed method.