A improved clustering analysis method based on fuzzy c-means algorithm by adding PSO algorithm

Authors:
Liang Pang;Kai Xiao;Alei Liang;Haibing Guan
Affiliations:
School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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Abstract

Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a local minimum of the objective function, thus lead to undesired segmentation results. To address this issue, an improved FCM which is based on clustering centroids updates with the use of particle swarm optimization (PSO) is proposed in this paper. This algorithm is designed to support multidimensional feature data and be accessible through parallel computation. The experimental results suggest that, compared to the conventional FCM algorithm, the proposed algorithm leads to higher chances of global optimum clustering and is less computationally intensive when large clustering number is needed.