
	Journal of Applied Sciences
	Year: 2009 \| Volume: 9 \| Issue: 5 \| Page No.: 854-864 DOI: 10.3923/jas.2009.854.864

Distributed Data Clustering Using Expectation Maximization Algorithm

B. Safarinejadian, M.B. Menhaj and M. Karrari

Abstract: In this study, a distributed expectation maximization (DEM) algorithm is first introduced in a general form for estimating parameters of a finite mixture of components. This algorithm is used for density estimation and clustering of the data distributed over the nodes of a network. Then, a distributed incremental EM algorithm (DIEM) with a higher convergence rate is proposed. After a full derivation of distributed EM algorithms, convergence of both DEM and DIEM algorithms is studied based on the negative free energy concept. It is shown that these algorithms increase the negative free energy incrementally at each node until reaching the convergence. Finally, the proposed algorithms are applied to cluster analysis of gene-expression data. Simulation results approve that DIEM remarkably outperforms DEM.

PDF Fulltext XML References Citation Report Citation

RELATED ARTICLES:

•	Parametric Estimation of the Immunes Proportion based on BCH Model and Exponential Distribution using Left Censored Data

How to cite this article:

B. Safarinejadian, M.B. Menhaj and M. Karrari, 2009. Distributed Data Clustering Using Expectation Maximization Algorithm. Journal of Applied Sciences, 9: 854-864.

DOI: 10.3923/jas.2009.854.864

URL: https://scialert.net/abstract/?doi=jas.2009.854.864

COMMENT ON THIS PAPER

Navigation


		Online First
		Current Issue
		Previous Issues
		Editorial Board
		Submit a Manuscript
		Guide to Authors
		Subscribe to E-alerts

Indexed In


		AGRIS
		ASCI-Database
		Asian Digital Library
		Chemical Abstract Services
		Google Scholar

Science Alert