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Object in real world are categorical in nature. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. In this study performance of cosine based hierarchical clustering algorithm for categorical data is evaluated. It make use of two functions such as Frequency Computation, Term Frequency based Cosine Similarity Matrix (TFCSM) computation. Clusters are formed using TFCSM based hierarchical clustering algorithm. Results are evaluated for vote real life data set using TFCSM based hierarchical clustering and standard hierarchical clustering algorithm using single link, complete link and average link method.

Term Frequency Based Cosine Similarity Measure for Clustering Categorical Data using Hierarchical Algorithm