The paper presents automatic clustering using Harmony Search based clustering algorithm. In this algorithm, the capability of Improved Harmony search is used to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony vector, our strategy is able to encode variable number of candidate cluster centers at each iteration. The CH cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. The proposed approach has been applied onto well-known datasets and experimental results show that the approach is able to find the appropriate number of clusters and locations of cluster centers.

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