求解数据聚类的全乌鸦搜索算法

Ze-Xue Wu, Ko-Wei Huang, A. S. Girsang
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引用次数: 8

摘要

数据聚类是一种众所周知的数据挖掘方法,通常用于最小化每个数据中心的内部距离,而最大化每个数据中心之间的距离。聚类问题已被证明是一个np困难问题。本文提出了一种基于整体优化算法(WOA)和Crow搜索算法(CSA)的混合算法,即HWCA。HWCA算法具有WOA和CSA搜索策略的优点。此外,还采用了混合个体算子和增强分集算子两种算子来提高解的质量。混合个体操作员使用轮盘赌方法来交换WOA和CSA系统中的个体。另一方面,HWCA执行增强分集操作,以提高每个系统的质量。此外,HWCA还与中心优化策略相结合,提高了各系统的多样性。在性能评价方面,提出的MPGO算法将WOA和CSA算法与6个著名的UCI基准进行比较。结果表明,该算法与比较算法相比具有更高的准确率。
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A Whole Crow Search Algorithm for Solving Data Clustering
Data clustering is a well-known data mining approach that usually used to minimizes the intra distance but maximizes inter distance of each data center. The cluster problem has been proved to be an NP-hard problem. In this paper, a hybrid algorithm based on Whole optimization algorithm (WOA) and Crow search algorithm (CSA) is proposed, namely HWCA. The HWCA algorithm has the advantages of the search strategy of the WOA and CSA. In addition to, there are two operators used to improve the quality of solution, namely hybrid individual operator and enhance diversity operator. The hybrid individual operator is used to exchanges individuals from the WOA and CSA systems by using the roulette wheel approach. In other hand, the HWCA performs enhance diversity operator to improve the quality of each system. More over, the HWCA is incorporated with center optimization strategy to enhance diversity of each system. In the performance evaluation, the proposed MPGO algorithm was comparison WOA and CSA algorithm with six well-known UCI benchmarks. The results show that the proposed algorithm has a higher measure of accuracy rate with comparison algorithms.
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