Clustering categorical data streams

Issue title: Advances in Intelligent Systems

Guest editors: Vassilis Kodogiannisx and Ilias Petrouniasy

Article type: Research Article

Authors: He, Zengyou^{a; *} | Xu, Xiaofei^b | Deng, Shengchun^b | Huang, Joshua Zhexue^c

Affiliations: [a] School of Software, Dalian University of Technology, Dalian, 116620, China | [b] Department of Computer Science and Engineering, Harbin Institute of Technology, P.O. Box 315, Harbin 150001, China | [c] Shenzhen Institute of Advanced Technology, Shenzhen, China | [x] University of Westminster, Westminster, UK | [y] The University of Manchester, Manchester, UK

Correspondence: [*] Corresponding author. E-mail: zyhe@dlut.edu.cn.

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.

Keywords: Clustering, categorical data, data stream, data mining

DOI: 10.3233/JCM-2011-0363

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 11, no. 4, pp. 185-192, 2011