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Chaotic sequence and opposition learning guided approach for data clustering

机译：混沌序列与反对派学习引导方法进行数据聚类

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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.
机译：数据群集是属于数据挖掘域的普遍存产。它旨在基于群集内距离的总和将给定数据对象分区给定的数据对象。这是一个难题的问题，已经提出了许多启发式方法来瞄准所需目标。然而，在搜索过程中，由于非线性目标函数和大量搜索域，本地夹紧的问题是普遍的。在本文中，在一种具有Real参数（Ieam-R）的改进的环境适应方法的快速融合进化算法中并入了反对学习和混沌序列引导方法，用于解决数据聚类问题。由正弦混沌图产生的混沌序列已被利用来针对搜索域中的有希望的解决方案。另一方面，包含对立基于学习的方法允许解决方案在搜索域中探索更合适的位置。将所提出的方法的性能与使用适应值，统计值，收敛曲线和箱图进行比较一些众所周知的算法。这些比较证明了建议的方法的功效。

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