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A hybrid stochastic genetic–GRASP algorithm for clustering analysis

Zopounidis Konstantinos, Michael Doumpos, Marinaki Magdalini, Marinakis Ioannis, Matsatsinis Nikolaos

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/167DFC2F-94BC-46B0-B4E6-800FF41DD71B
Έτος 2008
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά Y. Marinakis, M. Marinaki, M. Doumpos, N. Matsatsinis and C. Zopounidis, "A hybrid stochastic genetic–GRASP algorithm for clustering analysis," Operation. Res., vol. 8, no. 1, pp. 33-46, May 2008. doi:10.1007/s12351-008-0004-8 https://doi.org/10.1007/s12351-008-0004-8
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Περίληψη

This paper presents a new stochastic methodology, which is based on the concepts of genetic algorithms (GAs) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed stochastic algorithm (Hybrid GEN–GRASP) for the solution of the clustering problem is a two phase algorithm which combines a genetic algorithm for the solution of the feature selection problem and a GRASP algorithm 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 another methodology that uses for the solution of the feature selection problem a very popular metaheuristic method, the Tabu Search algorithm. Results from the application of the methodology to data sets from the UCI Machine Learning Repository are presented.

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