Competitive learning algorithms for vector quantization
Neural Networks
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In most of the clustering algorithms the number of clusters must be given in advance. However it's hard to do so without prior knowledge. The RPCL algorithm solves the problem by delearning the rival(the 2nd winner) every step, but its performance is too sensitive to the delearning rate. Moreover, when the clusters are not well separated, RPCL's performance is poor. In this paper We propose a RPFCL algorithm by associating a Fuzzy Inference System to the RPCL algorithm to tune the delearning rate. Experimental results show that RPFCL outperforms RPCL both in clustering speed and in achieving correct number of clusters.