Simultaneous feature selection and symmetry based clustering using multiobjective framework

Abstract

In this paper a new framework based on multiobjective optimization (MOO), namely FeaClusMOO, is proposed which is capable of identifying the correct partitioning as well as the most relevant set of features from a data set. A newly developed multiobjective simulated annealing based optimization technique namely archived multiobjective simulated annealing (AMOSA) is used as the background strategy for optimization. Here features and cluster centers are encoded in the form of a string. As the objective functions, two internal cluster validity indices measuring the goodness of the obtained partitioning using Euclidean distance and point symmetry based distance, respectively, and a count on the number of features are utilized. These three objectives are optimized simultaneously using AMOSA in order to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning. Points are allocated to different clusters using a point symmetry based distance. Mutation changes the feature combination as well as the set of cluster centers. Since AMOSA, like any other MOO technique, provides a set of solutions on the final Pareto front, a technique based on the concept of semi-supervised classification is developed to select a solution from the given set. The effectiveness of the proposed FeaClustMOO in comparison with other clustering techniques like its Euclidean distance based version where Euclidean distance is used for cluster assignment, a genetic algorithm based automatic clustering technique (VGAPS-clustering) using point symmetry based distance with all the features, K-means clustering technique with all features is shown for seven higher dimensional data sets obtained from real-life.