An improved FCM clustering method for interval data

Authors:
Shen-Ming Gu;Jian-Wen Zhao;Ling He
Affiliations:
School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, China;School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, China and Xiaoshan School, Zhejiang Ocean University, Hangzhou, China;School of Foreign Languages, Zhejiang Ocean University, Zhoushan, China
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

In fuzzy c-means (FCM) clustering algorithm, each data point belongs to a cluster with a degree specified by a membership grade. Furthermore, FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group so that the objective function is minimized. This paper introduces a clustering method for objects described by interval data. It extends the FCM clustering algorithm by using combined distances. Moreover, simulated experiments with interval data sets have been performed in order to show the usefulness of this method.