Combined Density-based and Constraint-based Algorithm for Clustering

We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules.Due to the merged clusters around the center cluster, the clustering results show high accu racy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA)proposed in 2004. Experimental results show that CDC has better performance.

作 者： CHEN Tung-shou CHEN Rong-chang LIN Chih-chiang CHIU Yung-hsing   作者單位： CHEN Tung-shou,LIN Chih-chiang,CHIU Yung-hsing(Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology, Taiwan China 40401)

CHEN Rong-chang(Department of Logistics Engineering and Management, National Taichung Institute of Technology, Taiwan China 40401) 

刊 名： 東華大學學報（英文版）  EI 英文刊名： JOURNAL OF DONGHUA UNIVERSITY(ENGLISH EDITION)  年，卷(期)： 2006 23(6)  分類號： O1  關鍵詞： K-means   Hierarchical clustering   Density-based clustering   Constraint-based clustering  

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