Cluster analysis plays a foremost role in identifying groups of genes that show similar behavior under a set of experimental conditions. Several clustering algorithms have been proposed for identifying gene behaviors and to understand their significance. The principal aim of this work is to develop an intelligent rough clustering technique, which will efficiently remove the irrelevant dimensions in a high-dimensional space and obtain appropriate meaningful clusters. This paper proposes a novel biclustering technique that is based on rough set theory. The proposed algorithm uses correlation coefficient as a similarity measure to simultaneously cluster both the rows and columns of a gene expression data matrix and mean squared residue to generate the initial biclusters. Furthermore, the biclusters are refined to form the lower and upper boundaries by determining the membership of the genes in the clusters using mean squared residue. The algorithm is illustrated with yeast gene expression data and the experiment proves the effectiveness of the method. The main advantage is that it overcomes the problem of selection of initial clusters and also the restriction of one object belonging to only one cluster by allowing overlapping of biclusters.

中文翻译：

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用于确定相关基因的基于粗糙集的合理聚类框架。

聚类分析在鉴定在一组实验条件下表现出相似行为的基因组中起着最重要的作用。已经提出了几种聚类算法来识别基因行为并理解它们的重要性。这项工作的主要目的是开发一种智能的粗糙聚类技术，该技术将有效地去除高维空间中无关的维度，并获得适当的有意义的聚类。本文提出了一种基于粗糙集理论的新颖的双聚类技术。所提出的算法使用相关系数作为相似性度量，以同时对基因表达数据矩阵的行和列以及均方残基进行聚类以生成初始双聚类。此外，通过使用均方残基确定簇中基因的成员关系，可优化二聚体以形成上下边界。用酵母基因表达数据对该算法进行了说明，实验证明了该方法的有效性。其主要优点是它克服了选择初始聚类的问题，并且通过允许双聚类的重叠，克服了仅属于一个聚类的一个对象的限制。