Deer hunting optimization technique for clustering unsupervised data in data mining

Abstract

Unsupervised data clustering investigation is a standout among the most valuable tools and is an informative task in data mining that looks to characterize similar articles’ gatherings. One of the eminent algorithms for the clustering field is K-means clustering. Scholars recommended enhancing the nature of K-means, and optimization algorithms were hybridized. In this study, a heuristic calculation, deer hunting optimization algorithm (DHOA), was adjusted for K-means data clustering by altering the fundamental parameters of DHOA calculation, which are propelled from the characteristic enlivened calculations. During this work, a new human-based descriptive DHOA has been developed following a human deer hunting strategy. In order to attack the fawn, hunters update their positions based on the movement of the leader and backward movement while also considering the angle of the deer. In this work, the DHOA was hybridized with K-means clustering and the performance of the proposed approach is tested against UCI repository data with different algorithms.