Clustering algorithm for proximity-relation matrix and its applications |
| |
Authors: | Wen-Liang Hung De-Hua Chen |
| |
Institution: | 1. Department of Applied Mathematics , National Hsinchu University of Education , Hsin-Chu , Taiwan, Republic of China;2. College of Information and Communications , Zhejiang Industry and Trade Vocational College , Wenzhou , People's Republic of China |
| |
Abstract: | In this paper, we present a new algorithm for clustering proximity-relation matrix that does not require the transitivity property. The proposed algorithm is first inspired by the idea of Yang and Wu 16] then turned into a self-organizing process that is built upon the intuition behind clustering. At the end of the process subjects belonging to be the same cluster should converge to the same point, which represents the cluster center. However, the performance of Yang and Wu's algorithm depends on parameter selection. In this paper, we use the partition entropy (PE) index to choose it. Numerical result illustrates that the proposed method does not only solve the parameter selection problem but also obtains an optimal clustering result. Finally, we apply the proposed algorithm to three applications. One is to evaluate the performance of higher education in Taiwan, another is machine–parts grouping in cellular manufacturing systems, and the other is to cluster probability density functions. |
| |
Keywords: | cellular manufacturing systems partition entropy proximity-relation matrix similarity clustering algorithm transitivity property |
|
|