Ensemble-based Multi-objective Clustering Algorithms for Gene Expression Data Sets

被引：0

作者：

Li, Jianxia ^{[1
]}

Liu, Ruochen ^{[1
]}

Zhang, Mingyang ^{[1
]}

Li, Yangyang ^{[1
]}

机构：

[1] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Shaanxi, Peoples R China

来源：

2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2017年

基金：

中国国家自然科学基金;

关键词：

multi-objective clustering ensemble algorithms; gene expression data; MOLECULAR CLASSIFICATION; PREDICTION; CANCER; DISCOVERY; PATTERNS; SUBTYPES;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, two multi-objective clustering ensemble algorithms are proposed named MOCLED and MOCNCD. MOCLED is different from MOCLE on three points. First, different clustering algorithms are used to produce some new individuals in evolutionary process. Second, a new screening mechanism is added. In each generation, the worst individual is replaced by the best individual. Third, a new objective function is added to ensure a diverse population. MOCNCD is the same as MOCLED except the crossover operator. We replace it with a new proposed cluster ensemble algorithm, IDICLENS. Experimental results reveal the advantages of our method on finding good partitions.

引用

页码：333 / 340

页数：8

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