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Regularized Gaussian Mixture Model based discretization for gene expression data association mining

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Abstract

Association rule has shown its usefulness in the gene expression data based disease diagnosis for its good interpretability. The large number of rules generated from the high dimensional gene expression data is one of the main challenges of its applications. In this work, we reveal that the discretization preprocessing is one of the reasons for the association rule number explosion problem. To alleviate this problem, a Regularized Gaussian Mixture Model (RGMM) is proposed to discretize the continuous gene expression data. RGMM explores both the complexity of the discretization model and the information loss of the discretization procedure, under the Minimal Description Length framework. Extensive experiments show the effectiveness of RGMM on real-life gene expression data sets.

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Acknowledgements

This work is financially supported by Natural Science Foundation of China (61070033, 61100148, 61202269), Natural Science Foundation of Guangdong Province (S2011040004804), Key Technology Research and Development Programs of Guangdong Province (2010B050400011), Opening Project of the State Key Laboratory for Novel Software Technology (KFKT2011B19), Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (LYM11060), Science and Technology Plan Project of Guangzhou City (12C42111607, 201200000031), Science and Technology Plan Project of Panyu District Guangzhou (2012-Z-03-67).

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Authors and Affiliations

  1. Faculty of Computer Science, Guangdong University of Technology, Guangzhou, P.R. China

    Ruichu Cai, Zhifeng Hao, Wen Wen & Lijuan Wang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, P.R. China

    Ruichu Cai

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  1. Ruichu Cai
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  2. Zhifeng Hao
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  3. Wen Wen
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  4. Lijuan Wang
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Correspondence to Ruichu Cai.

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Cai, R., Hao, Z., Wen, W. et al. Regularized Gaussian Mixture Model based discretization for gene expression data association mining. Appl Intell 39, 607–613 (2013). https://doi.org/10.1007/s10489-013-0435-7

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