Clustering in dynamic spatial databases

Zhang, Ji and Hsu, Wynne and Lee, Mong Li (2005) Clustering in dynamic spatial databases. Journal of Intelligent Information Systems, 24 (1). pp. 5-27. ISSN 0925-9902

Abstract

[Abstract]: Efficient clustering in dynamic spatial databases is currently an open problem with many potential applications. Most traditional spatial clustering algorithms are inadequate because they do not have an efficient support for incremental clustering.In this paper, we propose DClust, a novel clustering technique for dynamic spatial databases. DClust is able to provide multi-resolution view of the clusters, generate arbitrary shapes clusters in the presence of noise, generate clusters that are insensitive to ordering of input data and support incremental clustering efficiently. DClust utilizes the density criterion that captures arbitrary cluster shapes and sizes to select a number of representative points, and builds the Minimum Spanning Tree (MST) of these representative points, called R-MST. After the initial clustering, a summary of the cluster structure is built. This summary enables quick localization of the effect of data updates on the current set of clusters. Our experimental results show that DClust outperforms existing spatial clustering methods such as DBSCAN, C2P, DENCLUE, Incremental DBSCAN and BIRCH in terms of clustering time and accuracy of clusters found.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Awaiting Author's version, which may be deposited in accordance with the copyright policy of the publisher.
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 12 Aug 2009 05:25
Last Modified: 02 Jul 2013 23:22
Uncontrolled Keywords: spatial databases, data mining, multi-resolution clustering, incremental clustering, Minimum Spanning Tree
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: doi: 10.1007/s10844-005-0265-0
URI: http://eprints.usq.edu.au/id/eprint/5536

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