Approach of clustering ensemble in gene expression data based on Wavelet De-noising

XU Yuan; et al

doi:10.13406/j.cnki.cyxb.2010.05.048

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Approach of clustering ensemble in gene expression data based on Wavelet De-noising

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- Approach of clustering ensemble in gene expression data based on Wavelet De-noising
- Journal of Chongqing Medical University Vol. 35, Issue 5, Pages: 718-720(2010)
- 作者机构：
  
  重庆医科大学公共卫生学院卫生统计教研室
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.2010.05.048
  CLC： R319
- Published：2010，
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[1]徐源,彭斌,邓宇,田考聪.基于小波去噪的基因表达数据聚类集成算法的研究[J].重庆医科大学学报,2010,35(05):718-720.

XU Yuan, et al. Approach of clustering ensemble in gene expression data based on Wavelet De-noising. [J]. Journal of Chongqing Medical University 35(5):718-720(2010)
[1]徐源,彭斌,邓宇,田考聪.基于小波去噪的基因表达数据聚类集成算法的研究[J].重庆医科大学学报,2010,35(05):718-720. DOI： 10.13406/j.cnki.cyxb.2010.05.048.

XU Yuan, et al. Approach of clustering ensemble in gene expression data based on Wavelet De-noising. [J]. Journal of Chongqing Medical University 35(5):718-720(2010) DOI： 10.13406/j.cnki.cyxb.2010.05.048.

摘要

目的:为基因表达数据的聚类提供方法学参考。方法:提出了一种小波去噪与模糊聚类集成算法结合起来进行聚类的新算法对酵母细胞的基因表达数据进行聚类

并同模糊聚类集成算法进行比较分析。结果:采用Micro-precision方法对新算法和模糊聚类集成算法进行评价。结果表明本文提出的算法具有较高的聚类精度

并且聚类精度高于模糊聚类集成算法。结论:利用本文提出的算法对基因表达数据具有较好的聚类效果。

Abstract

Objective:To provide the methodology reference for the gene expression data clustering.Methods:This paper proposed a fuzzy clustering ensemble algorithm based on wavelet de-noising.The new method is applied to the yeast cell’s gene expression data

and the clustering results were compared with fuzzy ensemble clustering methods.Results:Apply micro-precision to evaluate the clustering results.The research indicated that the proposed method was superior to fuzzy ensemble clustering algotuthm.Conclusion:The proposed method has better clustering for the gene expression data clustering.

关键词

小波去噪模糊聚类集成学习基因表达

Keywords

Wavelet de-noisingFuzzy clusteringEnsemble learninggene expression

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