Opposition learning based Harris hawks optimizer for data clustering

Data clustering is a crucial machine learning technique that helps divide a given dataset into many similar data objects where the data members resemble each other. It is an unsupervised learning algorithm and is hugely applied in different machine learning and data mining applications. k-means algorithm is one of the popular methods for clustering the data. However, this algorithm is not much suitable as it causes the problem of local entrapment. To resolve such issues, nature-inspired algorithms (NIAs) came into existence. Harris hawks optimizer (HHO) is a recently developed NIA inspired by the chasing and collaborative behavior of Harris hawks in real nature. The efficacy of HHO has already been proved by researchers in solving complex problems of different domains. In this paper, an opposition-based learning HHO (OHHO) is proposed for data clustering. The performance of OHHO is compared against six well-known algorithms on ten benchmark datasets of the UCI machine learning repository. Experimental values have justified the effectiveness of the proposed approach.