Abstract
K-Modes is an eminent algorithm for clustering data set with categorical attributes. This algorithm is famous for its simplicity and speed. The KModes is an extension of the K-Means algorithm for categorical data. Since K-Modes is used for categorical data so ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Modes’ of clusters are used instead of ‘Means’.The major drawback of k-mode is that the user needs to define the centroid points. To overcome this problem, k-mode with entropy based similarity coefficient was introduced in order to find good initial center points and the accurate result of the clustering is to be obtained. The nature-inspired harmonic algorithm is hybridized to optimize the k-mode algorithm. Harmonic K-Mode Algorithm is proposed in this work that reduces the computation time and improves the accuracy for cluster generation. The performance is evaluated using different output parameters such as execution time, space complexity, accuracy, precision and recall on different datasets such as amazon book review, news aggregator, online retail, seed and wholesale consumer.
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