Work place: Faculty of Computer Applications, MRIU, Faridabad, India
E-mail: anupamaluthra@gmail.com
Website:
Research Interests: Data Mining, Data Structures and Algorithms
Biography
Ms. Anupama Chadha is a research scholar in Department of Computer Science and Engineering, Manav Rachna International University. Her area of interest include Data Mining, Software Engineering.
By Anupama Chadha Suresh Kumar
DOI: https://doi.org/10.5815/ijitcs.2016.03.06, Pub. Date: 8 Mar. 2016
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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