Empirical Study on Clustering of Gene Expression Dataset-its Prediction and Mining

Our ability to assemble genome-wide expression data has far exceeded the ability of our diminutive human brains to analyses the raw data. We can refine the data down to a more understandable level by grouping the genes into a smaller number of categorization and then analyzing those. Clustering is one of the most popular techniques in gene expression. Many clustering algorithms have been initiated to analyze gene expression data, but less supervision is available to help choose among them. The evaluation of feasible and applicable clustering algorithms is becoming an important issue in today’s bioinformatics research. It is a method of grouping data into different clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The basic clustering algorithms are K-mean and K-medoids. This paper presents a literature survey on the clustering application in gene expression. An attempt is made to provide a guide for the researchers who are working in the area of gene expression and data clustering.