ABSTRACT Data clustering is an important data analysis and data mining tool in many fields such as pattern recognition and image processing. The goal of data clustering is to optimally organize similar objects into clusters. Grey wolf optimizer is a newly introduced optimization algorithm with inspiration from the social behavior of gray wolves. In this work, we propose a modified gray wolf optimizer to tackle some of the challenges in meta-heuristic algorithms. These modifications include a balanced approach to the exploration and exploitation stages of the algorithm as well as a local search around the best solution found. The performance of the proposed algorithm is compared to seven other clustering methods on nine data sets from the UCI machine learning laboratory. Experimental results demonstrate the competence of the proposed algorithm in solving data clustering problems. Overall, the intra-cluster distance of the proposed algorithm is lower than other algorithms and gives an average error rate of 11.22% which is the lowest among all.

中文翻译：

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一种改进的基于灰狼优化器的数据聚类算法

摘要 数据聚类是模式识别、图像处理等诸多领域中重要的数据分析和数据挖掘工具。数据聚类的目标是将相似的对象最佳地组织成簇。灰狼优化器是一种新引入的优化算法，其灵感来自灰狼的社会行为。在这项工作中，我们提出了一种改进的灰狼优化器来解决元启发式算法中的一些挑战。这些修改包括算法探索和开发阶段的平衡方法以及围绕找到的最佳解决方案的局部搜索。在来自 UCI 机器学习实验室的九个数据集上，将所提出算法的性能与其他七种聚类方法进行了比较。实验结果证明了该算法在解决数据聚类问题方面的能力。总体而言，该算法的簇内距离低于其他算法，平均错误率为 11.22%，是所有算法中最低的。