A New Adaptive Elastic Net Method for Cluster Analysis

IAENG Internaitonal journal of computer science

Lei Zhang¹  Junyan Yi¹  Gang Yang²  Peixin Zhao³ 

[1] Beijing University of Civil Engineering and Architecture;School of InformationRenmin University of China59 Zhongguancun StreetBeijing, 100872, China;Shandong University

关键词: self-organizing neural network;    elastic net;    adaptive;    cluster analysis;   

DOI  :  10.15837/ijccc.2017.3.2796

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 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.

【 授权许可】

CC BY