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  • 标题:A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application
  • 本地全文:下载
  • 作者:Shoulin Yin ; Jie Liu ; Lin Teng
  • 期刊名称:TELKOMNIKA (Telecommunication Computing Electronics and Control)
  • 印刷版ISSN:2302-9293
  • 出版年度:2016
  • 卷号:14
  • 期号:3
  • 页码:1150-1156
  • DOI:10.12928/telkomnika.v14i3.3805
  • 语种:English
  • 出版社:Universitas Ahmad Dahlan
  • 摘要: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.
  • 其他摘要: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.
  • 关键词:Biclustering algorithm; Variational Bayesian; Joint distribution probability; Semi-supervised clustering
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