Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant Colony Optimization
Expanding Neighborhood GRASP for the Traveling Salesman Problem
Computational Optimization and Applications
Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment
Journal of Global Optimization
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A Hybrid Clustering Algorithm Based on Multi-swarm Constriction PSO and GRASP
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Learning and Intelligent Optimization
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
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Clustering is a very important problem that has been addressed in many contexts and by researchers in many disciplines. This paper presents a new stochastic nature inspired methodology, which is based on the concepts of Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm (Hybrid PSO-GRASP) for the solution of the clustering problem is a two phase algorithm which combines a PSO algorithm for the solution of the feature selection problem and a GRASP for the solution of the clustering problem. Due to the nature of stochastic and population-based search, the proposed algorithm can overcome the drawbacks of traditional clustering methods. Its performance is compared with other popular stochastic/ metaheuristic methods like genetic algorithms and tabu search. Results from the application of the methodology to a survey data base coming from the Paris olive oil market and to data sets from the UCI Machine Learning Repository are presented.