A New Adaptive Elastic Net Method for Cluster Analysis

  • Junyan Yi Beijing University of Civil Engineering and Architecture
  • Peixin Zhao Shandong University
  • Lei Zhang Beijing University of Civil Engineering and Architecture
  • Gang Yang School of Information Renmin University of China 59 Zhongguancun Street Beijing, 100872, China

Abstract

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.

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Published
2017-04-23
How to Cite
YI, Junyan et al. A New Adaptive Elastic Net Method for Cluster Analysis. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 3, p. 429-441, apr. 2017. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2796>. Date accessed: 21 apr. 2021. doi: https://doi.org/10.15837/ijccc.2017.3.2796.

Keywords

self-organizing neural network;elastic net;adaptive;cluster analysis