¹International Business Faculty, Beijing Normal University, Zhu Hai, China
²School of Management, Harbin Institute of Technology, Harbin, China
³School of Sports Leisure, Beijing Normal University, Zhu Hai, China

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Abstract—Due to fast development of network technique, internet users have to face to massive textual data every day. Because of unsupervised merit of clustering, clustering is a good solution for users to analyze and organize texts into categories. However, most of recent clustering algorithms conduct in static situation. That indicates, it doesn’t allow clustering algorithm to deal with novel data efficiently. When novel data appear, traditional clustering algorithms can’t change their structure easily. Obviously, this restrict is not fit to internet, since novel data appear at any time. For this reason, an incremental clustering algorithm is proposed in this paper to cluster incremental data. This algorithm has two factors. (a) It designs two measures to calculate feature’s ability and integrate them in similarity measurement by replacing concurrence based similarity measurements. (b) Based on proposed similarity measurement, this algorithm selects few samples from original texts to perform incremental clustering. Experimental results demonstrate that, after integrating feature’s capacity, our algorithm can obtain high quality to cluster texts.

Index Terms—Self-organizing-mapping, feature’s intracluster representative ability, feature’s inter-cluster discriminable ability, similarity calculation

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Cite: Lei Chen, Bao-Jin Zhao, Li-Na Zhao, "A SOM based Incremental Clustering Algorithm," Journal of Computers vol. 9, no. 3, pp. 601-607, 2014.