| Combined Density-based and Constraint-based Algorithm for Clustering |
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| 作者姓名： | 陈同孝 陈荣昌 林志强 邱永兴 |
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| 作者单位： | [1]Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology, Taiwan China 40401 [2]Department of Logistics Engineering and Management, National Taichung Institute of Technology, Taiwan China 40401 |
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| 摘 要： | 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.
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| 关 键 词： | 分析方法 聚类 密度理论 约束理论 |
| 收稿时间： | 2006-08-20 |
| 修稿时间： | 2006-08-20 |
Combined Density-based and Constraint-based Algorithm for Clustering |
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| CHEN Tung-shou,CHEN Rong-chang,LIN Chih-chiang,CHIU Yung-hsing.Combined Density-based and Constraint-based Algorithm for Clustering[J].Journal of Donghua University,2006,23(6):36-38,61. |
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| Authors: | CHEN Tung-shou CHEN Rong-chang LIN Chih-chiang CHIU Yung-hsing |
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| Affiliation: | 1. Graduate School of Computer Science and Information Technology, National Taichung Institute of Technology, Taiwan China 40401
2. Department of Logistics Engineering and Management, National Taichung Institute of Technology, Taiwan China 40401 |
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| Abstract: | 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 accuracy. 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. |
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| Keywords: | K-means Hierarchical clustering Density-based clustering Constraint-based clustering |
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