Contents 1. Introduction
Although has been widely used, FCM has two major limitations: (1) The number of clusters should be determined in advance. (2) FCM is sensitive to initial centers and easy to fall into local minimum.
Abstract:
This paper presents a robust fuzzy clustering algorithm which can perform clustering without pre-assigning the number of clusters and is not sensitive to the initializati...Show MoreMetadata
Abstract:
This paper presents a robust fuzzy clustering algorithm which can perform clustering without pre-assigning the number of clusters and is not sensitive to the initialization of cluster centers. This is achieved by iteratively splitting and merging operations under the guidance of mistake measurements. In every step of the iteration, we first split the cluster containing data points belonging to different classes, and then merge the wrongly divided cluster pair. A validity index is proposed based on the two mistake measurements to determine the termination of the clustering process. Experimental results confirm the effectiveness and robustness of the proposed clustering algorithm.
Date of Conference: 11-15 November 2012
Date Added to IEEE Xplore: 14 February 2013
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Conference Location: Tsukuba, Japan
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