| DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases |
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| 作者姓名： | ZHOU Jiaogen GUAN Jihong LI Pingxiang |
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| 作者单位： | ZHOU Jiaogen GUAN Jihong LI Pingxiang ZHOU Jiaogen，State Key Laboratory of Information Engineering in Surveying，Mapping and Remote Sensing，Wuhan University，129 Luoyu Road，Wuhan 430079，China. |
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| 基金项目： | Funded by the National 973 Program of China (No.2003CB415205)，the National Natural Science Foundation of China (No.40523005, No.60573183, No.60373019)，the Open Research Fund Program of LIESMARS (No.WKL(04)0303). |
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| 摘 要： | Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.
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| 关 键 词： | 分布式空间信息数据库 双重聚类算法 DCAD 知识发现 数据挖掘 |
| 文章编号： | 1009-5020（2007）02-137-08 |
| 收稿时间： | 19 March 2007 |
| 修稿时间： | 2007-03-19 |
DCAD: a dual clustering algorithm for distributed spatial databases |
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| ZHOU Jiaogen GUAN Jihong LI Pingxiang.DCAD: a dual clustering algorithm for distributed spatial databases[J].Geo-Spatial Information Science,2007,10(2):137-144. |
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| Authors: | Zhou Jiaogen Guan Jihong Li Pingxiang |
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| Institution: | (1) State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China |
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| Abstract: | Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient. |
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| Keywords: | distributed clustering dual clustering distributed spatial database |
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