A novel clustering algorithm based on gravity and cluster merging

Authors:
Jiang Zhong;Longhai Liu;Zhiguo Li
Affiliations:
College of Computer Science, Chongqing University, Chongqing, China;College of Computer Science, Chongqing University, Chongqing, China;Shanghai Baosight Software Corporation, Chongqing, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Fuzzy C-means (FCM) clustering algorithm is commonly used in data mining tasks. It has the advantage of producing good modeling results in many cases. However, it is sensitive to outliers and the initial cluster centers. In addition, it could not get the accurate cluster number during the algorithm. To overcome the above problems, a novel FCM algorithm based on gravity and cluster merging was presented in this paper. By using gravity in this algorithm, the influence of outliers was minimized and the initial cluster centers were selected. And by using cluster merging, an appropriate number of clustering could be specified. The experimental evaluation shows that the modified method can effectively improve the clustering performance.