Microarray Analysis Using Fuzzy C-Means Clustering Algorithm

Abstract: The technology of DNA microarrays has become the most sophisticated and the most widely used among other microarrays. This paper shows the feature of microarray analysis and the expanded information of DNA microarray analysis. The clustering technique is the process of finding a structured data from unlabeled data. It is a grouping process of dividing the data in groups of similar type and it contains different types of clusters like hierarchical, exclusive, overlapping and probabilistic. Each group is referred to as a cluster which contains objects of similar type. The data set for processing is taken from UCI Machine Learning Repository website and the analysis is done using the WEKA tool (Waikato Environment for Knowledge Analysis) which is an effective tool for machine learning. WEKA tool is Java-Based version which contains the collection of visualization tools and algorithms like clustering, classification, regression, preprocessing etc for data analysis. In this paper the microarray dataset is taken for predicting Breast cancer with the help of Fuzzy C-Means clustering technique.

Keywords: Microarray Analysis, DNA Microarray analysis, Cluster Analysis, Fuzzy C-Means Algorithm.