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A New Evolutionary Algorithm For Cluster Analysis
2008
Zenodo
Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The
doi:10.5281/zenodo.1329264
fatcat:r3gfwbsuzvehvo6n7kko2447hu