A particle swarm clustering algorithm with fuzzy weighted step sizes

Szabo A.; Delgado M.R.; De Castro L.N.

A particle swarm clustering algorithm with fuzzy weighted step sizes

Tipo

Artigo de evento

Data de publicação

2015

Periódico

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Citações (Scopus)

2

Autores

Szabo A.
Delgado M.R.
De Castro L.N.

Resumo

© Springer International Publishing Switzerland 2015.This paper proposes a modification in the Fuzzy Particle Swarm Clustering (FPSC) algorithm such that membership degrees are used to weight the step size in the direction of the local and global best particles, and in its movement in the direction of the input data at every iteration. This results in the so-called Membership Weighted Fuzzy Particle Swarm Clustering (MWFPSC). The modified algorithm was applied to six benchmark datasets from the literature and its results compared to that of the standard FPSC and FCM algorithms. By introducing these modifications it could be observed a gain in accuracy, representativeness of the clusters found and the final Xie-Beni index, at the expense of a slight increase in the practical computational time of the algorithm.

Assuntos Scopus

Benchmark datasets , Computational time , FCM algorithm , FPSC , Fuzzy particle swarm , Membership degrees , Modified algorithms , Particle swarm