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Journal of Applied Sciences

Year: 2009 | Volume: 9 | Issue: 5 | Page No.: 854-864
DOI: 10.3923/jas.2009.854.864

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Authors


B. Safarinejadian

Country: Iran

M.B. Menhaj

Country: Iran

M. Karrari

Country: Iran

Keywords


  • Distributed data mining
  • EM algorithm
  • cluster analysis
  • density estimation
  • gene-expression data
Research Article

Distributed Data Clustering Using Expectation Maximization Algorithm

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

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