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Some Metaheuristic Approaches for the Clustering Problem with an Application to Failure Detection

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2 Author(s)
Adriana Marcucci Bustos ; Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá, Colombia. ; Alain Gauthier Sellier

Clustering is a relevant problem that takes place in many practical environments. This paper presents some meta-heuristic approaches as an alternative to the traditional clustering techniques, like K-means or C-means. They are based on some metaheuristic optimization algorithms as tabu search, simulated annealing, genetic algorithms and ant colony. The developed techniques have the advantage that they could escape more efficiently from local minima. Additionally, an application on failure detection in a hydraulic system was developed and the obtained results are competitive with some well known techniques

Published in:

2006 IEEE International Conference on Information Reuse & Integration

Date of Conference:

16-18 Sept. 2006