关键词: 三维微阵列 三维聚类 多目标进化 双聚类 数据挖掘

Abstract:

The clustering technique is widely used in microarray data analysis, and mining three-dimensional（3D） clusters in gene-sample-time（simply GST） mic roarray data is emerging as a hot research topic in this area. During the mining of 3D clusters, several objectives have to be optimized simultaneously, and often these objectives are in conflict with each other. Moreover, with great exploration power, evolutionary computation is made as an effective se arch approach in the search space of huge dimensionality. Based on MOEA（Multi-Objective Evolutionary Algorithm）, this paper proposes a new 3D cluster    algorithm, MOE-TC（Multi-Objective Evolutionary TriClustering）, tO mine 3D clusters in 3D mieroarray data. Experimental results in real microarray dat  ta confirm the validity of the proposed technique.

Key words: 3D microarray, triclustering, multi-objective evolutionary, biclustering, data