Journal
JOURNAL OF APPLIED STATISTICS
Volume 40, Issue 9, Pages 1875-1892Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2013.799126
Keywords
cellular manufacturing systems; partition entropy; proximity-relation matrix; similarity clustering algorithm; transitivity property
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Funding
- National Science Council of the Republic of China [NSC 97-2118-M-134-002]
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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.
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