| 基于邻域互信息和K均值的基因选择算法 |
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| 引用本文： | 殷樱,张玉冰,刘家诚,高昆. 基于邻域互信息和K均值的基因选择算法[J]. 数字社区&智能家居, 2014, 0(2): 821-823 |
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| 作者姓名： | 殷樱 张玉冰 刘家诚 高昆 |
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| 作者单位： | 河南师范大学计算机与信息工程学院,河南新乡453007 |
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| 基金项目： | 大学生创新实验项目河南师范大学校级重点项目（2012年）《邻域互信息在基因数据挖掘中的应用研究》编号1s |
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| 摘 要： | 多数传统的属性聚类算法不能直接处理连续型属性，为了避免连续数据离散化处理时造成的信息损失，降低样本属性邻域求解的复杂度，提高特征基因提取的效率。文中提出一种将邻域互信息用于属性聚类的特征基因选择方法，用于在海量的基因表达谱数据中挖掘出少量的具有分类识别能力且冗余度较小的特征基因。
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| 关 键 词： | 粒计算 邻域互信息 属性聚类 基因选择 |
Based on Neighborhood Mutual Information and Mean Genetic Algorithm |
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| YIN Ying,ZHANG Yu-bing,LIU Jia-cheng,GAO Kun. Based on Neighborhood Mutual Information and Mean Genetic Algorithm[J]. Digital Community & Smart Home, 2014, 0(2): 821-823 |
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| Authors: | YIN Ying ZHANG Yu-bing LIU Jia-cheng GAO Kun |
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| Affiliation: | (College of Computer and Information Technology, Henan Normal University, Xinxiang 453007, China) |
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| Abstract: | The majority of traditional clustering algorithms can not deal directly with the properties of continuous attributes, in order to avoid loss of information when the discrete continuous data processing caused by the sample properties to reduce the complexity of solving the neighborhood, and improve the efficiency of extraction feature gene. This paper proposes a neighbor-hood mutual information for the property clustering feature gene selection method for digging out a small number of genes char-acterized by the ability to identify and classify the smaller mass redundancy in gene expression profiling data. |
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| Keywords: | Granular Computing neighborhood mutual information attribute clustering gene selection |
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