Journal of Intelligent Information Systems

, Volume 24, Issue 1, pp 5-27

First online:

Clustering in Dynamic Spatial Databases

  • Ji ZhangAffiliated withSchool of Computing, National University of Singapore
  • , Wynne HsuAffiliated withSchool of Computing, National University of Singapore
  • , Mong Li LeeAffiliated withSchool of Computing, National University of Singapore Email author 

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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.


spatial databases data mining multi-resolution clustering incremental clustering Minimum Spanning Tree