Hybrid Fuzzy C-Means Clustering Algorithm, Improving Solution Quality and Reducing Computational Complexity

Abstract

Fuzzy C-Means is a clustering algorithm widely used in many applications. However, its computational complexity is very large, which prevents its use for large problem instances. Therefore, a hybrid improvement is proposed for the algorithm, which considerably reduces the number of iterations and, in many cases, improves the solution quality, expressed as the value of the objective function. This improvement integrates two heuristics, one in the initialization phase and the other in the convergence phase or the convergence criterion. This improvement was called HPFCM. A set of experiments was designed to validate this proposal; to this end, four sets of real data were solved from a prestigious repository. The solutions obtained by HPFCM were compared against those of the Fuzzy C-Means algorithm. In the best case, reductions of an average of 97.65% in the number of required iterations and an improvement in quality solution of 82.42% were observed when solving the SPAM dataset. Finally, we consider that the proposed heuristics may inspire improvements in other specific purpose variants of Fuzzy C-Means.

Keywords

• computational complexity
• convergence
• efficiency
• Fuzzy C-Means
• hybrid clustering algorithm
• initialization