A novel clustering algorithm based upon a SOFM neural network family

Authors:
Junhao Wen;Kaiwen Meng;Hongyan Wu;Zhongfu Wu
Affiliations:
College of Software Engineering, Chongqing University and College of Science, Chongqing University, Chongqing, China;College of Science, Chongqing University, Chongqing, China;College of Computer Science, Chongqing University, Chongqing, China;College of Computer Science, Chongqing University, Chongqing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

A novel clustering algorithm based upon a SOFM neural network family is proposed in this paper. The algorithm takes full advantage of the characteristics of SOFM Neural Network family and defines a novel similarity measure, topological similarity, which help the clustering algorithm to handle the clusters with arbitrary shapes and avoid suffering from the limitations of the conventional clustering algorithms. The paper suggests another novel thought to tackle the clustering problem.