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A novel hybrid multi-verse optimizer with K-means for text documents clustering
Neural Computing and Applications ( IF 6 ) Pub Date : 2020-05-11 , DOI: 10.1007/s00521-020-04945-0
Ammar Kamal Abasi , Ahamad Tajudin Khader , Mohammed Azmi Al-Betar , Syibrah Naim , Zaid Abdi Alkareem Alyasseri , Sharif Naser Makhadmeh

Text clustering has been widely utilized with the aim of partitioning specific document collection into different subsets using homogeneity/heterogeneity criteria. It has also become a very complicated area of research, including pattern recognition, information retrieval, and text mining. Metaheuristics are typically used as efficient approaches for the text clustering problem. The multi-verse optimizer algorithm (MVO) involves a stochastic population-based algorithm. It has been recently proposed and successfully utilized to tackle many hard optimization problems. However, a recently applied research trend involves hybridizing two or more algorithms with the aim of obtaining a superior solution regarding the problems of optimization. In this paper, a new hybrid of MVO algorithm with the K-means clustering algorithm is proposed, i.e., the H-MVO algorithm with the aims of enhancing the quality of initial candidate solutions, as well as the best solution, which is produced by MVO at each iteration. This hybrid algorithm aims at improving the global (diversification) ability of the search and finding a better cluster partition. The proposed H-MVO effectiveness was tested on five standard datasets, which are used in the domain of data clustering, as well as six standard text datasets, which are utilized in the domain of text document clustering, in addition to two scientific articles’ datasets. The experiments showed that K-means hybridized MVO improves the results in terms of high convergence rate, accuracy, error rate, purity, entropy, recall, precision, and F-measure criteria. In general, H-MVO has outperformed or at least proven to be highly competitive compared to the original MVO algorithm and with well-known optimization algorithms like KHA, HS, PSO, GA, H-PSO, and H-GA and the clustering techniques like K-mean, K-mean++, DBSCAN, agglomerative, and spectral clustering techniques.



中文翻译:

一种新颖的带有K-means的混合多词优化器,用于文本文档聚类

文本聚类已被广泛使用,其目的是使用同质性/异质性标准将特定的文档集合划分为不同的子集。它也已成为一个非常复杂的研究领域,包括模式识别,信息检索和文本挖掘。元启发法通常用作解决文本聚类问题的有效方法。多宇宙优化器算法(MVO)涉及一种基于随机种群的算法。最近已经提出并成功地使用它来解决许多困难的优化问题。然而,近来应用的研究趋势涉及混合两种或更多种算法,目的是获得关于优化问题的优良解决方案。本文提出了一种新的MVO算法与K-means聚类算法的混合体,即 H-MVO算法旨在提高初始候选解决方案以及最佳解决方案的质量,该解决方案是由MVO在每次迭代中生成的。该混合算法旨在提高搜索的全局(多样化)能力并找到更好的集群分区。除了两个科学文章的数据集外,还对五个标准数据集(用于数据聚类领域)以及六个标准文本数据集(用于文本文档聚类领域)进行了测试,验证了拟议的H-MVO有效性。 。实验表明,K均值杂交MVO在高收敛速度,准确性,错误率,纯度,熵,召回率,精确度和F度量标准方面均改善了结果。一般来说,

更新日期:2020-05-11
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