Reactive search-MST optimized clustering-based feature selection

Abstract

Data clustering is a technique for analyzing the data that is incurred in various fields such as data processing, pattern recognition, knowledge discovery and machine learning. Feature clustering is an important paradigm for different types of feature selection techniques that aims to reduce redundant and irrelevant features from a given set of features in order to maintain load balance on the classification algorithm. The work proposed a PSO–GSO–MST, a hybrid approach that combines Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO). The work performs efficient feature selection with improved classification accuracy. Clustering analysis plays an important role in knowledge discovery and data mining. It adopts the unsupervised learning method, and the results of clustering are similar within the class and are different between the classes. Aiming at some shortcomings of traditional clustering algorithms, some techniques for clustering using natural heuristic algorithms have emerged. The proposed work performs cluster using optimized Minimum Spanning Tree (MST). The work aims to perform optimization of MST with the help of two renowned techniques such as PSO and GSO. The proposed PSO–GSO–MST is compared with state-of-the-art algorithms such as Clustering-based Feature Selection (CFS) and PSO–MST. The results show that the classification accuracy for the proposed PSO–GSO–MST performs better by 16.9% than CFS and by 4.7% than PSO–MST optimized CFS, respectively. The outcome of the work proves that the proposed algorithm achieves improved performance than the currently available algorithms and can be used for clustering applications.