doi: 10.7763/IJET.2010.V2.144
Parameter Selection for EM Clustering Using Information Criterion and PDDP
Ujjwal Das Gupta
,
Vinay Menon
,
Uday Babbar
- Department of Computer Engineering, Delhi College of Engineering, India
Abstract
This paper presents an algorithm to automatically determine the number of clusters in a given input data set, under a mixture of Gaussians assumption. Our algorithm extends the Expectation- Maximization clustering approach by starting with a single cluster assumption for the data, and recursively splitting one of the clusters in order to find a tighter fit. An Information Criterion parameter is used to make a selection between the current and previous model after each split. We build this approach upon prior work done on both the K-Means and Expectation-Maximization algorithms. We extend our algorithm using a cluster splitting approach based on Principal Direction Divisive Partitioning, which improves accuracy and efficiency
Keywords
- clustering
- expectation-maximization
- mixture of Gaussians
- principal direction divisive partitioning
How to Cite
Ujjwal Das Gupta, Vinay Menon, and Uday Babbar, "Parameter Selection for EM Clustering Using Information Criterion and PDDP," International Journal of Engineering and Technology, vol. 2, no. 4, pp. 340-344, 2010. https://doi.org/ 10.7763/IJET.2010.V2.144
Copyright & License
Copyright © 2010 by the authors. 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 (CC BY 4.0).