A new initialization method for clustering categorical data

Authors:
Shu Wu;Qingshan Jiang;Joshua Zhexue Huang
Affiliations:
School of Software, Xiamen University, Xiamen, China;School of Software, Xiamen University, Xiamen, China;E-Business Technology Institute, The University of Hong Kong, Hong Kong
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

Performance of partitional clustering algorithms which converges to numerous local minima highly depends on initial cluster centers. This paper presents an initialization method which can be implemented to partitional clustering algorithms for categorical data sets with minimizing the numerical objective function. Experimental results show that the new initialization method is more efficient and stabler than the traditional one and can be implemented to large data sets for its linear time complexity.