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Proceedings of the 2nd International Symposium on Information Processing (ISIP 2009)

Huangshan, China, August 21-23, 2009

Editors: Fei Yu, Jian Shu, and Guangxue Yue

AP Catalog Number: AP-PROC-CS-09CN002

ISBN: 978-952-5726-02-2 (Print), 978-952-5726-03-9 (CD-ROM)

Page(s): 175-178

A Fast Incremental Clustering Algorithm

Xiaoke Su, Yang Lan, Renxia Wan, and Yuming Qin

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Clustering has played a very important role in data mining. In this paper, a fast incremental clustering algorithm is proposed by changing the radius threshold value dynamically. The algorithm restricts the number of the final clusters and reads the original dataset only once. At the same time an inter-cluster dissimilarity measure taking into account the frequency information of the attribute values is introduced. It can be used for the categorical data. The experimental results on the mushroom dataset show that the proposed algorithm is feasible and effective. It can be used for the large-scale data set.

Index Terms

incremental clustering, categorical data, radius threshold value, inter-cluster dissimilarity measure, clustering accuracy, data mining

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