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Abstract
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Article Information:
A Precise Distance Metric for Mixed Data Clustering using Chi-square Statistics
S. Mohanavalli and S.M. Jaisakthi
Corresponding Author: S. Mohanavalli
Submitted: May 1, 2015
Accepted: May 10, 2015
Published: August 25, 2015 |
Abstract:
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In today's scenario, data is available as a mix of numerical and categorical values. Traditional data clustering algorithms perform well for numerical data but produce poor clustering results for mixed data. For better partitioning, the distance metric used should be capable of discriminating the data points with mixed attributes. The distance measure should appropriately balance the categorical distance as well as numerical distance. In this study we have proposed a chi-square based statistical approach to determine the weight of the attributes. This weight vector is used to derive the distance matrix of the mixed dataset. The distance matrix is used to cluster the data points using the traditional clustering algorithms. Experiments have been carried out using the UCI benchmark datasets, heart, credit and vote. Apart from these data sets we have also tested our proposed method using a real-time bank data set. The accuracy of the clustering results obtained are better than those of the existing works.
Key words: Chi-square statistics, clustering, mixed data attributes, , , ,
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Cite this Reference:
S. Mohanavalli and S.M. Jaisakthi, . A Precise Distance Metric for Mixed Data Clustering using Chi-square Statistics. Research Journal of Applied Sciences, Engineering and Technology, (12): 1441-1444.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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