Competitive learning algorithms for data clustering
Georgeta Budura, Corina Botoca, Nicolae Miclău · Facta universitatis - series Electronics and Energetics · 2006
This paper presents and discusses some competitive learning algorithms for data clustering. A new competitive learning algorithm, named the dynamically penalized rival competitive learning algorithm (DPRCL), is introduced and studied. It is a variant of the rival penalized competitive algorithm [1] and it performs appropriate clustering without knowing the clusters number, by automatically driving the extra seed points far away from the input data set. It does not have the 'dead units' problem. Simulations results, performed in different conditions, are presented showing that the performance of the new DPRCL algorithm is better comparative with other competitive algorithms.