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Title: Clustering in dynamic spatial databases
Authors: Zhang, J. 
Hsu, W. 
Lee, M.L. 
Keywords: Data mining
Incremental clustering
Minimum Spanning Tree
Multi-resolution clustering
Spatial databases
Issue Date: 2005
Citation: Zhang, J., Hsu, W., Lee, M.L. (2005). Clustering in dynamic spatial databases. Journal of Intelligent Information Systems 24 (1) : 5-27. ScholarBank@NUS Repository.
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. © 2005 Springer Science + Business Media, Inc.
Source Title: Journal of Intelligent Information Systems
ISSN: 09259902
DOI: 10.1007/s10844-005-0265-0
Appears in Collections:Staff Publications

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