Abstract:

A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy similarity between samples the essential associations among samples are modeled factually. The fuzzy similarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy similarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of faster convergence rate and more exact clustering results than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.

Key words: dynamic fuzzy clustering, fuzzy similarity matrix, genetic algorithm