Data clustering is a prevalent problem that belongs to the data mining domain. It aims to partition the given data objects into some specified number of clusters based on the sum of the intra-cluster distances. It is an NP-hard problem, and many heuristic approaches have already been proposed to target the desired objective. However, during the search process, the problem of local entrapment is prevalent due to nonlinear objective functions and a large range of search domains. In this paper, an opposition learning and chaotic sequence guided approaches are incorporated in a fast converging evolutionary algorithm called improved environmental adaptation method with real parameter (IEAM-R) for solving the data clustering problem. A chaotic sequence generated by a sinusoidal chaotic map has been utilized to target promising solutions in the search domain. On the other hand, the inclusion of the opposition learning-based approach allows the solutions to explore more appropriate locations in the search domain. The performance of the proposed approach is compared against some well-known algorithms using fitness values, statistical values, convergence curves, and box plots. These comparisons justify the efficacy of the suggested approach.

数据集群是一个普遍的问题，属于数据挖掘领域。它旨在根据集群内距离的总和将给定的数据对象划分为一些指定数量的集群。这是一个NP难题，并且已经提出了许多启发式方法来瞄准期望的目标。然而，在搜索过程中，由于非线性目标函数和大范围的搜索域，局部陷入问题普遍存在。本文将对立学习和混沌序列导引方法结合到一种快速收敛的进化算法中，该算法被称为带实参数的改进环境适应方法（IEAM-R），用于解决数据聚类问题。由正弦混沌图生成的混沌序列已被用于在搜索域中定位有希望的解决方案。另一方面，基于对立学习的方法的包含使解决方案能够探索搜索域中更合适的位置。使用适合度值，统计值，收敛曲线和箱形图，将所提出的方法的性能与一些知名算法进行比较。这些比较证明了所建议方法的有效性。