A genetic k-medoids clustering algorithm |
| |
Authors: | Weiguo Sheng Xiaohui Liu |
| |
Institution: | (1) Department of Electronics, University of Kent, Canterbury, Kent, CT2 7NT, UK;(2) Department of Information System and Computing, Brunel University, UB8 3PH London, UK |
| |
Abstract: | We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search.
Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified
Davies-Bouldin index as a measure of the fitness of the corresponding partitionings. As a result the proposed algorithm can
efficiently evolve appropriate partitionings while making no a priori assumption about the number of clusters present in the datasets. In the experiments, we show the effectiveness of the proposed
algorithm and compare it with other related clustering methods. |
| |
Keywords: | k-Medoids clustering Genetic algorithms Heuristics Cluster validity Davies-Bouldin index |
本文献已被 SpringerLink 等数据库收录! |
|