An annealing-evolution technique for clustering
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An efficient partitional clustering technique, called Annealing-Evolution-clustering (ANEV-clustering), and its fuzzy version, that integrate the power of simulated annealing for obtaining minimum energy configuration, and the searching capability of evolutionary programming are proposed in this article. Two other evolutionary programming based clustering techniques are also developed where Gauss and Cauchy mutation strategies have been used. The clustering methodology is used to search for appropriate cluster centers in multi-dimensional feature space such that a similarity metric of the resulting clusters is optimized. In ,AN.EV-clustering, data points are redistributed among the clusters probabilistically in the mutation phase of the evolution process, so that points that are farther away from the cluster center have higher probabilities of migrating to other clusters than those which are closer to it. The superiority of the AN EV -clustering algorithm over the widely used fc-means algorithm, simulated annealing and conventional evolutionary programming based clustering algorithms is extensively demonstrated for artificial and real life data sets. For the fuzzy clustering algorithm, we have compared the results with the well known fuzzy c-means algorithm. The proposed crisp clustering method is also used for classifying the pixels of a satellite image of a part of the city of Kolkata.
Bibliogr. 27 poz.
- Dept. of Computer Science and Engineering, Kalyani Govt. Engg. College, Kalyani 741235, India, firstname.lastname@example.org
- Machine Intelligence Unit, Indian Statistical Institute, 203, B.T. Road, Calcutta 700 108, India , email@example.com
- Dept. of Computer Science, Jadavpur University, Kolkata 700 032, India , firstname.lastname@example.org
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