Model Based Clustering Using Evolutionary Algorithm

R. A. Radhika*, **
*Department of Information Technology, Kongu Engineering College, Perundurai, India
**Assistant Professor, Dept of Information Technology, Kongu Engineering College, Perundurai, India
Periodicity:September - November'2013
DOI : https://doi.org/10.26634/jit.2.4.2540

Abstract

Clustering is collection of data objects that are similar to one another and thus can be treated collectively as one group. The model based clustering approach uses model for clustering and optimizes the fit between the data and model. The evolutionary algorithm has the ability to thoroughly search the parameter space, providing an approach inherently more robust with respect to local maxima. In EvolvExpectation-Maximization(EvolvEM)algorithm,Expectation Maximization and Genetic algorithm is used for clustering data which shows more efficiency then EM clustering. The drawback in this method is that its execution time is higher and it requires more parameters. In the proposed approach, instead of Genetic algorithm, Bee colony optimization can be combined with Expectation Maximization algorithm in order to improve execution time and clustering efficiency. Hence, it can be efficiently used for clustering.

Keywords

Clustering; EM algorithm; Genetic Algorithm; Fitness function; Evolv EM clustering.

How to Cite this Article?

Radhika, R. A., and Priya, D. R. (2013). Model based Clustering using Evolutionary Algorithm. i-manager’s Journal on Information Technology, 2(4), 16-20. https://doi.org/10.26634/jit.2.4.2540

References

[2]. Charles Bouveyron, Stephane Girard and Cordelia Schmid, (2007). "High-dimensional data clustering," Computer Statistics Data Analysis, 52 (1), 502–519,
[3]. Dimitris Karlis, Anais Santourian, (2009). "Model- based clustering with non-elliptically contoured distributions," Statistical Computer, 19, 73–83.
[4]. Franz Pernkopf, Djamel Bouchaffra, (2005). "Geneticbased EM algorithm for learning Gaussian mixture models, "IEEE Transaction Pattern Analysis Machine Intelligence, 27, 1344–1348.
[5]. Kalyanmoy Deb, (2001). "Multi- Objective Optimization Using Evolutionary Algorithms," Wiley- Interscience series in systems and optimization, John Wiley & Sons.
[6]. Jeffrey L. Andrews, Paul D. McNicholas (2013), 'Using evolutionary algorithms for model-based clustering', Pattern Recognition Letters ,34 , 987–992 .
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