Clustering Using Finite Geometric Skew Normal Mixture Models

Deepana R¹ , Kiruthika C²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.3 , pp.136-143, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i3.136143

Online published on Jun 30, 2019

Copyright © Deepana R , Kiruthika C . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Finite mixtures of non-normal distributions using model based clustering approach have emerged as an effective tool in modeling heterogeneous data with asymmetric features. In particular, an attention is paid to the geometric skew normal mixture models which have been used for skewed data. Parameter estimation is carried out using Expectation Maximization (EM) algorithm. Euclidean distance based initialization technique is used for finding initial mixing proportion values. Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) are used to identify the optimal model. The clustering performance of these mixture models have been illustrated with the help of real and simulated datasets.

Key-Words / Index Term :
Geometric skew normal distribution, Finite Mixture Model, Model based Clustering, EM algorithm.

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