A new competitive learning algorithm for data clustering

Abstract

This paper presents a new competitive learning algorithm for data clustering, named the dynamically penalized rival competitive learning algorithm (DPRCA). It is a variant of the rival penalized competitive algorithm and it performs appropriate clustering without knowing the clusters number, by automatically driving extra seed points far away from the input data set. It doesn’t have the "dead neurons" problem. The performances of the DPRCA algorithm were tested by simulations carried out considering different conditions of noise.