Study of Sensitive Parameters of PSO Application to Clustering of Texts
Reda Mohamed Hamou, Abdelmalek Amine, Ahmed Chaouki Lokbani · International Journal of Applied Evolutionary Computation · 2013
In this paper, the authors study the parameter sensitivity of the technique of particles warm optimization (PSO) for the clustering of data, in particular the text. They experienced the PSO parameters by varying within a range of research and we noted the best result of clustering based on three measures of assessment, internal, which is the index of Davies and Bouldin and two external based on recall and precision that are the F-measure and entropy. Every time they finished an experimentation of a parameter, it is fixed to its optimal value for the next experiment parameters. The results showed a high sensitivity of some parameters on the result of clustering.