Improving penalized regression-based clustering model in big data

Abstract

Abstract Clustering is the main procedure for data mining with a wide application such as gene analysis. Clustering is a method of separates (grouping) previously unclassified data on the basis of its features, and it is an unsupervised learning problem that divides that data into groups in such a way that it makes those data in the same group more similar to each other compared to in other groups. Penalized regression-based clustering is an extension of the “Sum Of Norms” clustering model. In this paper, the nature-inspired algorithm is employed to improve the penalized regression-based clustering to better estimation. The real data application on gene expression data results suggests that our proposed improvement can bring significant improvement relative to others.