Abstract
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.
Highlights
Data mining deals with extracting information from a data source and transform it into a valuable knowledge for further use
Program for cosine similarity and term frequency based cosine similarity computation written in java language
Categorical data are not analyzed as numerical data because of the absence of inherit ordering
Summary
Data mining deals with extracting information from a data source and transform it into a valuable knowledge for further use. Hierarchical clustering algorithms group data objects to form a tree shaped structure. It can be broadly classified into agglomerative hierarchical clustering and divisive hierarchical clustering. Agglomerative approach is called as bottom up approach, where each data points are considered a separate cluster. Clusters are merged based on certain criteria. Divisive approach otherwise called as top down approach, where all data points considered as a single cluster and they are split into number of clusters based on certain criteria.
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