Abstract
In this paper a new technique is integrated to Multi-Objective Particle
Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN)
topology, to produce MOPSO-PN algorithm. This technique involves
iteratively selecting a set of best solutions from the
Pareto-Optimal-Fronts and trying to explore them in order to find better
clustering results in the next iteration. MOPSO-PN was then used as a
Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested
on various datasets (real-life and artificial datasets). Two scenarios
have been used to test the performances of MOPSO-PN for clustering: In
the first scenario MOPSO-PN utilizes, as objective functions, two
clusters validity index (Silhouette?Index and
overall-cluster-deviation), three datasets for test, four algorithms for
comparison and the average Minkowski Score as metric for evaluating the
final clustering result; In the second scenario MOPSO-PN used, as
objectives functions, three clusters validity index (I-index, Con-index
and Sym?index), 20 datasets for test, ten algorithms for comparison and
the F-Measure as metric for evaluating the final clustering result. In
both scenarios, MOPSO-PN provided a competitive clustering results and a
correct number of clusters for all datasets.