Data clustering is one of the important techniques of data mining that is responsible for dividing N data objects into K clusters while minimizing the sum of intra-cluster distances and maximizing the sum of inter-cluster distances. Due to nonlinear objective function and complex search domain, optimization algorithms find difficulty during the search process. Recently, Harris hawks optimization (HHO) algorithm is proposed for solving global optimization problems. HHO has already proved its efficacy in solving a variety of complex problems. In this paper, a chaotic sequence-guided HHO (CHHO) has been proposed for data clustering. The performance of the proposed approach is compared against six state-of-the-art algorithms using 12 benchmark datasets of the UCI machine learning repository. Various comparative performance analysis and statistical tests have justified the effectiveness and competitiveness of the suggested approach.

数据聚类是数据挖掘的重要技术之一，负责将N个数据对象划分为K个聚类，同时最小化集群内距离的总和并最大化集群间距离的总和。由于非线性目标函数和复杂的搜索域，优化算法在搜索过程中会遇到困难。近年来，提出了哈里斯霍克斯优化（HHO）算法来解决全局优化问题。HHO已经证明了其解决各种复杂问题的功效。在本文中，提出了一种混沌序列引导的HHO（CHHO）用于数据聚类。使用UCI机器学习存储库的12个基准数据集，将所提方法的性能与6种最新算法进行了比较。各种比较性能分析和统计测试证明了该方法的有效性和竞争力。