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A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application

Journal from gdlhub / 2016-11-09 03:53:51
Oleh : Shoulin Yin, Jie Liu, Lin Teng, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : Biclustering algorithm, Variational Bayesian, Joint distribution probability, Semi-supervised clustering
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/3805

Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semi-supervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper’s new method is better than relevant biclustering algorithms for biclustering analysis.

Deskripsi Alternatif :

Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semi-supervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper’s new method is better than relevant biclustering algorithms for biclustering analysis.

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