标题:A New Adaptive Elastic Net Method for Cluster Analysis
作者:Yi, J.; Zhao, P.; Zhang, L.; Yang, G.
作者机构:[Yi, J.] Beijing Univ Civil Engn & Architecture, Beijing Key Lab Robot Bion & Funct Res, Dept Comp Sci & Technol, 1 Zhanlanguan Rd, Beijing 100044, Pe 更多
通讯作者:Zhao, P
通讯作者地址:[Zhao, P]Shandong Univ, Sch Management, 27 Shanda Nan Rd, Jinan 250100, Peoples R China.
来源:INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
出版年:2017
卷:12
期:3
页码:429-441
DOI:10.15837/ijccc.2017.3.2796
关键词:self-organizing neural network; elastic net; adaptive; cluster analysis
摘要:Clustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently.
收录类别:SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018830905&doi=10.15837%2fijccc.2017.3.2796&partnerID=40&md5=9b184eeabb41d908fc60455bdb138636
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