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Hybrid Clustering Algorithm based on Mahalanobis Distance and MST

V. Valli Kumari, Bhvs Ramakrishnam Raju, Azad Naik Published in Data Mining

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450549
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  1. Valli V Kumari, Bhvs Ramakrishnam Raju and Azad Naik. Article: Hybrid Clustering Algorithm based on Mahalanobis Distance and MST. International Journal of Applied Information Systems 3(5):60-63, July 2012. BibTeX

    @article{key:article,
    	author = "V. Valli Kumari and Bhvs Ramakrishnam Raju and Azad Naik",
    	title = "Article: Hybrid Clustering Algorithm based on Mahalanobis Distance and MST",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 5,
    	pages = "60-63",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Most of the clustering algorithms are based on Euclidean distance as measure of similarity between data objects. Theses algorithms also require initial setting of parameters as a prior, for example the number of clusters. The Euclidean distance is very sensitive to scales of variables involved and independent of correlated variables. To conquer these drawbacks a hybrid clustering algorithm based on Mahalanobis distance is proposed in this paper. The reason for the hybridization is to relieve the user from setting the parameters in advance. The experimental results of the proposed algorithm have been presented for both synthetic and real datasets.

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Keywords

Minimum Spanning Tree, Fuzzy, Mahalanobis