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Hybrid Clustering Algorithm based on Mahalanobis Distance and MST

by V. Valli Kumari, Bhvs Ramakrishnam Raju, Azad Naik
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 5
Year of Publication: 2012
Authors: V. Valli Kumari, Bhvs Ramakrishnam Raju, Azad Naik
10.5120/ijais12-450549

V. Valli Kumari, Bhvs Ramakrishnam Raju, Azad Naik . Hybrid Clustering Algorithm based on Mahalanobis Distance and MST. International Journal of Applied Information Systems. 3, 5 ( July 2012), 60-63. DOI=10.5120/ijais12-450549

@article{ 10.5120/ijais12-450549,
author = { V. Valli Kumari, Bhvs Ramakrishnam Raju, Azad Naik },
title = { Hybrid Clustering Algorithm based on Mahalanobis Distance and MST },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2012 },
volume = { 3 },
number = { 5 },
month = { July },
year = { 2012 },
issn = { 2249-0868 },
pages = { 60-63 },
numpages = {9},
url = { https://www.ijais.org/archives/volume3/number5/232-0549/ },
doi = { 10.5120/ijais12-450549 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:45:50.704479+05:30
%A V. Valli Kumari
%A Bhvs Ramakrishnam Raju
%A Azad Naik
%T Hybrid Clustering Algorithm based on Mahalanobis Distance and MST
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 3
%N 5
%P 60-63
%D 2012
%I Foundation of Computer Science (FCS), NY, 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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Index Terms

Computer Science
Information Sciences

Keywords

Minimum Spanning Tree Fuzzy Mahalanobis

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