Particle Swarm Optimization applied to relational data clustering
Renê Pereira de Gusmão, Francisco de A.T. de Carvalho · 2016
This work introduces a hard clustering algorithm based on Particle Swarm Optimization metaheuristic that is able to partition objects considering their relational descriptions given by a single dissimilarity matrix. The PSO is a metaheuristic based on population which is well known for its simplicity, good performance and it was already designed as clustering algorithm for vector data. The proposed PSO algorithm uses a modified version of the HCMdd algorithm as local search. The HCMdd algorithm is a variant of the well known hard K-medoids clustering algorithm for relational data, that is designed to provide a partition and a representative for each cluster. The performance and the usefullness of the proposed algorithm, in comparison with HCMdd, RHCM and Spectral clustering algorithms, these last two are also able to work with relational data, are illustrated with suitable normalized data sets from the UCI Machine Learning Repository.