A Competitive and Cooperative Learning Approach to Robust Data Clustering

Y.-M. Cheung (PRC)


Cooperative and Competitive Learning, Semi-Competitive Learning, Rival Penalization Controlled Competitive Learning, Clustering Analysis, Cluster Number.


This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to adapt to an input each time, but also dynam ically cooperate to achieve the learning task. This compet itive and cooperative mechanism can automatically merge those extra seed points, meanwhile making the seed points gradually converge to the corresponding cluster centers. Consequently, CCL can perform a robust clustering analy sis without prior knowing the exact cluster number so long as the number of seed points is not less than the true one. The experiments have successfully shown its outstanding performance on data clustering.

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