Harris Hawks Optimizer for Data Clustering

One of the key methods of data mining is data clustering, which divides data objects or data points (N) into clusters (K) while maximizing the sum of the distance between clusters and reducing intra-cluster distance. Optimization algorithms are used in data clustering problems, and in general, they struggle in the search process because of the huge or complex search domain and nonlinear objective function. In order to solve global optimization problems, a novel method called the Harris Hawks Optimization (HHO) technique has been proposed. HHO has demonstrated its effectiveness in resolving a number of challenging issues; therefore, HHO has been selected for data clustering problems. In the proposed method, two benchmark datasets from the UCI machine learning library were used to evaluate the results of the proposed HHO method, which were then compared with four other state-of-the-art algorithms. The comparative performance analysis supports the effectiveness and competitiveness of the proposed strategy.