Cluster analysis is one important field in pattern recognition and machine learning, consisting in an attempt to distribute a set of data patterns into groups, considering only the inner properties of those data. One of the most popular techniques for data clustering is the K-Means algorithm, due to its simplicity and easy implementation. But K-Means is strongly dependent on the initial point of the search, what may lead to suboptima (local optima) solutions. In the past few decades, Evolutionary Algorithms (EAs), like Group Search Optimization (GSO), have been adapted to the context of cluster analysis, given their global search capabilities and flexibility to deal with hard optimization problems. However, given their stochastic nature, EAs may be slower to converge in comparison to traditional clustering models (like K-Means). In this work, three hybrid memetic approaches between K-Means and GSO are presented, named FMKGSO, MKGSO and TMKGSO, in such a way that the global search capabilities of GSO are combined with the fast local search performances of K-Means. The degree of influence of K-Means on the behavior of GSO method is evaluated by a set of experiments considering both real-world problems and synthetic data sets, using five clustering metrics to access how good and robust the proposed hybrid memetic models are.

聚类分析是模式识别和机器学习中的一个重要领域，它试图将一组数据模式分布到各个组中，而只考虑那些数据的内部属性。数据聚类最流行的技术之一是K-Means算法，因为它简单易行。但是K-Means强烈依赖于搜索的起始点，这可能导致次优（局部最优）解决方案。在过去的几十年中，进化搜索算法（EA）（例如组搜索优化（GSO））已经适应了聚类分析的背景，因为它们具有全局搜索功能并且可以灵活地处理难题。但是，鉴于其随机性，与传统的聚类模型（如K-Means）相比，EA收敛速度可能会更慢。在这项工作中 提出了K-Means和GSO之间的三种混合模因方法，分别称为FMKGSO，MKGSO和TMKGSO，以使GSO的全局搜索功能与K-Means的快速本地搜索性能相结合。通过考虑真实世界问题和综合数据集的一组实验，使用五个聚类指标来评估所提出的混合模因模型的优良性和健壮性，通过一组实验评估了K-Means对GSO方法行为的影响程度。