A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application
Abstract: 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.
Keywords: biclustering
algorithm, variational bayesian, joint distribution probability,
semi-supervised clustering
Author: Shoulin Yin
Journal Code: jptkomputergg160156
