Cluster analysis is a valuable data analysis and data mining technique. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems including data clustering. In this paper, a recently proposed algorithm called the water cycle algorithm, based on the evaporation rate is used in conjunction with a local search method namely Hookes and Jeeves method to perform data clustering. Statistical analyses were carried out which show that the hybrid optimization method, in general, performs superior to the methods reported in the literature in terms of solution quality as well as computational performance. The proposed hybrid algorithm is tested on some selected standard datasets obtained from the UCI machine-learning repository. The objective function is based on the Euclidean distance as well as the DB index. The experimental results were compared with the data clustering results reported in published literature. The simulation results confirm the superiority of the proposed hybrid method as an efficient and reliable algorithm to solve clustering problems.

聚类分析是一种有价值的数据分析和数据挖掘技术。自然启发式的基于人口的元启发法是解决包括数据聚类在内的优化问题的有前途的搜索方法。在本文中，最近提出的基于蒸发速率的算法称为水循环算法，与局部搜索方法（即Hookes和Jeeves方法）结合使用，以进行数据聚类。进行的统计分析表明，混合优化方法通常在解决方案质量和计算性能方面都优于文献中报道的方法。在从UCI机器学习存储库获得的一些选定标准数据集上对提出的混合算法进行了测试。目标函数基于欧几里得距离以及DB索引。将实验结果与已发表文献中报道的数据聚类结果进行了比较。仿真结果证实了所提出的混合方法作为解决聚类问题的有效且可靠算法的优越性。