Unsupervised machine learning approach for gene expression microarray data using soft computing technique

Abstract

Machine learning is a burgeoning technology used for extractions of knowledge from an ocean of data. It has robust binding with optimization and artificial intelligence that delivers theory, methodologies and application domain to the field of statistics and computer science. Machine learning tasks are broadly classified into two groups namely supervised learning and unsupervised learning. The analysis of the unsupervised data requires thorough computational activities using different clustering algorithms. Microarray gene expression data are taken into consideration for cluster regulating genes from non-regulating genes. In our work optimization technique (Cat Swarm Optimization) is used to minimize the number of cluster by evaluating the Euclidean distance among the centroids. A comparative study is being carried out by clustering the regulating genes before optimization and after optimization. In our work Principal component analysis (PCA) is incorporated for dimensionality reduction of vast dataset to ensure qualitative cluster analysis.