Clustering categorical data streams

Authors:
Zengyou He;Xiaofei Xu;Shengchun Deng;Joshua Zhexue Huang
Affiliations:
School of Software, Dalian University of Technology, Dalian, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China;Shenzhen Institute of Advanced Technology, Shenzhen, China
Venue:
Journal of Computational Methods in Sciences and Engineering
Year:
2011

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Abstract

In this paper, we propose an efficient clustering algorithm for analyzing categorical data streams. It has been proved that the proposed algorithm uses small memory footprints. We provide empirical analysis on the performance of the algorithm in clustering both synthetic and real data streams.