An Optimal Configuration of Sensitive Parameters of PSO Applied to Textual Clustering

Abstract

The clustering aims to minimize intra-class distance in the cluster and maximize extra-classes distances between clusters. The text clustering is a very hard task; it is solved generally by metaheuristic. The current literature offers two major metaheuristic approaches: neighborhood metaheuristics and population metaheuristics. In this chapter, the authors seek to find the optimal configuration of sensitive parameters of the PSO algorithm applied to textual clustering. The study will go through in dissociable steps, namely the representation and indexing textual documents, clustering by biomimetic approach, optimized by PSO, the study of parameter sensitivity of the optimization technique, and improvement of clustering. The authors will test several parameters and keep the best configurations that return the best results of clustering. They will use the most widely used evaluation measures like index of Davies and Bouldin (internal) and two external: the F-measure and entropy, which are based on recall and precision.