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

Shoulin Yin, Jie Liu, Lin Teng


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 papers new method is better than relevant biclustering algorithms for biclustering analysis.


Biclustering algorithm, Variational Bayesian, Joint distribution probability, Semi-supervised clustering

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DOI: http://dx.doi.org/10.12928/telkomnika.v14i3.3805

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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