The microarray technology enables the analysis of the gene expression data and the understanding of the important biological processes in an efficient way. We have developed an efficient clustering scheme for microarray gene expression data based on correlation-based feature selection, ant-based clustering, fuzzy c-means algorithm and a novel heaps merging heuristic. The algorithm utilizes the feature selection algorithm to overcome the high-dimensionality problem encountered in bioinformatics domain. Based on extensive empirical analysis on microarray data, clustering quality of the ant-based clustering algorithm is enhanced with the use of fuzzy c-means algorithm and heaps merging heuristic. The performance of the proposed clustering scheme is compared with k-means, PAM algorithm, CLARA, self-organizing map, hierarchical clustering, divisive analysis clustering, self-organizing tree algorithm, hybrid hierarchical clustering, consensus clustering, AntClass algorithm and fuzzy c-means clustering algorithms. The experimental results indicate that the proposed clustering scheme yields better performance in clustering cancer gene expression data.

微阵列技术可以有效地分析基因表达数据并了解重要的生物学过程。我们基于基于相关性的特征选择，基于蚂蚁的聚类，模糊c均值算法和一种新颖的合并启发式算法，为微阵列基因表达数据开发了有效的聚类方案。该算法利用特征选择算法来克服生物信息学领域中遇到的高维问题。基于对微阵列数据的广泛实证分析，通过使用模糊c均值算法和堆合并启发式算法，提高了基于蚂蚁的聚类算法的聚类质量。将提出的聚类方案的性能与k均值，PAM算法，CLARA，自组织图，分层聚类，划分分析聚类，自组织树算法，混合层次聚类，共识聚类，AntClass算法和模糊c均值聚类算法。实验结果表明，提出的聚类方案在聚类癌症基因表达数据中表现出更好的性能。