Data Mining is the extraction of useful information from a huge amount of datasets. As one of the most important tasks in data mining, clustering aims to group a set of objects such that the objects within the same cluster are more similar to each other than to the objects in another cluster. An extension of the K-Means Algorithm, K-Mode Algorithm, is partitioning based clustering algorithm does not guarantee for the optimal solution. To overcome this problem, entropy based similarity coefficient was introduced in order to find good initial center points and the accurate result of the clusters were obtained. The nature-inspired harmonic algorithm is hybridized to optimize the k-mode algorithm. In this paper, Harmonic K-Mode Algorithm is proposed that reduces the computation time and improves the accuracy for cluster generation. The experimental result shows that the proposed algorithm gives better results than the existing algorithms.

Data Mining, Clustering, K-Means Algorithm, K-Mode Algorithm

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