In this paper, we have devised a multiobjective optimization solution framework for solving the problem of gene expression data clustering in reduced feature space. Here clustering problem is viewed from two different aspects: clustering of genes in reduced sample space or clustering of samples in reduced gene space. Three objective functions: two internal cluster validity indices and the count on the number of features are optimized simultaneously by a popular multiobjective simulated annealing based approach, namely AMOSA. Here, point symmetry based distance is used for the assignment of gene data points to different clusters. Seven publicly available benchmark gene expression data sets are used for experimental purpose. Both aspects of clustering in reduced feature space is demonstrated. The proposed gene expression clustering technique outperforms the existing nine clustering techniques. Apart from this, also some statistical and biological significant tests have been carried out to show that the proposed FSC-MOO technique is more statistically and biologically enriched

在本文中，我们设计了一个多目标优化解决方案框架，用于解决特征空间缩小的基因表达数据聚类问题。这里的聚类问题是从两个不同的方面来看的：缩小样本空间中的基因聚类或缩小基因空间中的样本聚类。三个目标函数：两个内部聚类有效性指标和特征数量的计数通过一种流行的基于多目标模拟退火的方法即AMOSA同时进行优化。在此，基于点对称性的距离用于将基因数据点分配给不同的簇。七个可公开获得的基准基因表达数据集用于实验目的。演示了在缩小的特征空间中聚类的两个方面。提出的基因表达聚类技术优于现有的九种聚类技术。除此之外，还进行了一些统计和生物学上的重大测试，以表明所提出的FSC-MOO技术在统计和生物学上都更加丰富