Extension of K-Modes Algorithm for Generating Clusters Automatically

Extension of K-Modes Algorithm for Generating Clusters Automatically

Автор: Anupama Chadha, Suresh Kumar

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 3 Vol. 8, 2016 года.

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K-Modes is an eminent algorithm for clustering data set with categorical attributes. This algorithm is famous for its simplicity and speed. The K-Modes 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'. However, one major limitation of this algorithm is dependency on prior input of number of clusters K, and sometimes it becomes practically impossible to correctly estimate the optimum number of clusters in advance. In this paper we have proposed an algorithm which will overcome this limitation while maintaining the simplicity of K-Modes algorithm.

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Clustering, K-Modes clustering, Dependency, Prior input, Number of clusters

Короткий адрес: https://sciup.org/15012464

IDR: 15012464

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