An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement

doi:10.1016/j.jksuci.2022.04.010

CQUET-IR

	An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement
	Li, Teng 1; Rezaeipanah, Amin 2; El Din, ElSayed M. Tag 3
	2022-07-01
发表期刊	JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES (IF:13.473[JCR-2020],8.078[5-Year])
ISSN	1319-1578
EISSN	2213-1248
卷号	34 期号:6 页码:3828-3842
摘要	The advent of architectures such as the Internet of Things (IoT) has led to the dramatic growth of data and the production of big data. Managing this often-unlabeled data is a big challenge for the real world. Hierarchical Clustering (HC) is recognized as an efficient unsupervised approach to unlabeled data analysis. In data mining, HC is a mechanism for grouping data at different scales by creating a dendrogram. One of the most common HC methods is Agglomerative Hierarchical Clustering (AHC) in which clusters are created bottom-up. In addition, ensemble clustering approaches are used today in complex problems due to the weakness of individual clustering methods. Accordingly, we propose a clustering framework using AHC methods based on ensemble approaches, which includes the clusters clustering technique and a novel similarity measurement. The proposed algorithm is a Meta-Clustering Ensemble scheme based on Model Selection (MCEMS). MCEMS uses the bi-weighting policy to solve the model selection associated problem to improve ensemble clustering. Specifically, multiple AHC individual methods cluster the data from different aspects to form the primary clusters. According to the results of different methods, the similarity between the instances is calculated using a novel similarity measurement. The MCEMS scheme involves the creation of meta-clusters by re-clustering of primary clusters. After clusters clustering, the number of optimal clusters is determined by merging similar clusters and considering a threshold. Finally, the similarity of the instances to the meta-clusters is calculated and each instance is assigned to the meta-cluster with the highest similarity to form the final clusters. Simulations have been performed on some datasets from the UCI repository to evaluate MCEMS scheme compared to state-of-the-art algorithms. Extensive experiments clearly prove the superiority of MCEMS over HMM, DSPA and WHAC algorithms based on Wilcoxon test and Cophenetic correlation coefficient. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
关键词	Hierarchical clustering Meta-clusters Ensemble clustering Model selection Similarity measurement Clusters clustering
DOI	10.1016/j.jksuci.2022.04.010
收录类别	SCIE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000836430400002
出版者	ELSEVIER
原始文献类型	Article
出版地	AMSTERDAM
引用统计	被引频次：51[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/14063
专题	重庆电子科技职业大学
作者单位	1.Chongqing Coll Elect Engn, Artificial Intelligence & Big Data Coll, Chongqing 401331, Peoples R China; 2.Persian Gulf Univ, Dept Comp Engn, Bushehr, Iran; 3.Future Univ Egypt, Fac Engn & Technol, Elect Engn Dept, New Cairo 11845, Egypt
推荐引用方式 GB/T 7714	Li, Teng,Rezaeipanah, Amin,El Din, ElSayed M. Tag. An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement[J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,2022,34(6):3828-3842.
APA	Li, Teng,Rezaeipanah, Amin,&El Din, ElSayed M. Tag.(2022).An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement.JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,34(6),3828-3842.
MLA	Li, Teng,et al."An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement".JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 34.6(2022):3828-3842.