Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/2FCC6232-9E01-4327-941B-91B819C5AF2F | ||
| Έτος | 2007 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | Y. Marinakis, M. Marinaki, N. Matsatsinis ,"A hybrid particle Swarm optimization algorithm for clustering analysis,"in 2007 9th Intern. Conf. (DaWaK),pp.241-250.doi:10.1007/978-3-540-74553-2_22 https://doi.org/10.1007/978-3-540-74553-2_22 | ||
| Εμφανίζεται στις Συλλογές |
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.
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