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Clustering the Mixed Numerical and Categorical Dataset Using Similarity Weight and Filter Method

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Executive Summary

Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. In this paper, the authors cluster the mixed numeric and categorical data set in efficient manner.

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