An improved sequential clustering algorithm

Authors:
Yingxia Liu;Bo Gao;Xingming Zhang
Affiliations:
School of Computer Science & Engineering South China University of Technology, Guangzhou, P.R. China;Information Center of the Economic & Information Commission of Guangdong Province;School of Computer Science & Engineering South China University of Technology, Guangzhou, P.R. China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

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Abstract

In this paper, it designs an improved sequential clustering approach, which compensates for shortcomings in existing algorithms. This method uses bisecting k-means clustering framework and reduces the computing time through adding the cosine similarity comparison when sequences can not satisfy the pruning condition, while the accuracy is still in an acceptable range.