Quantum behaved particle swarm optimization for data clustering with multiple objectives

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Heyam Al-Baity
  • Souham Meshoul
  • Ata Kaban
  • Lilac Al Safadi

Colleges, School and Institutes

Abstract

Clustering is an important tool in many fields such as exploratory data mining and pattern recognition. It consists in organizing a large data set into groups of objects that are more similar to each other than to those in other groups. Despite its use for over three decades, it is still subject to a lot of controversy. In this paper, we cast clustering as a Pareto based multi-objective optimization problem which is handled using a quantum behaved particle swarm optimization algorithm. The search process is carried out over the space of cluster centroids with the aim to find partitions that optimize two objectives simultaneously, namely compactness and connectivity. Global best leader selection is performed using a hybrid method based on sigma values and crowding distance. The proposed algorithm has been tested using synthetic and real data sets and compared to the state of the art methods. The results obtained are very competitive and display good performance both in terms of the cluster validity measure and in terms of the ability to find trade-off partitions especially in the case of close clusters.

Details

Original languageEnglish
Title of host publication6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014
Publication statusPublished - 12 Jan 2015
Event6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014 - Tunis, Tunisia
Duration: 11 Aug 201414 Aug 2014

Conference

Conference6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014
CountryTunisia
CityTunis
Period11/08/1414/08/14

Keywords

  • Clustering, F-measure, Multi objective optimization, Quantum behaved particle swarm optimization