Clustering objects on a spatial network

Man Lung Yiu, Nikos Mamoulis

Research output: Journal article publicationConference articleAcademic researchpeer-review

107 Citations (Scopus)


Clustering is one of the most important analysis tasks in spatial databases. We study the problem of clustering objects, which lie on edges of a large weighted spatial network. The distance between two objects is defined by their shortest path distance over the network. Past algorithms are based on the Euclidean distance and cannot be applied for this setting. We propose variants of partitioning, density-based, and hierarchical methods. Their effectiveness and efficiency is evaluated for collections of objects which appear on real road networks. The results show that our methods can correctly identify clusters and they are scalable for large problems.
Original languageEnglish
Pages (from-to)443-454
Number of pages12
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
Publication statusPublished - 27 Jul 2004
Externally publishedYes
EventProceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2004 - Paris, France
Duration: 13 Jun 200418 Jun 2004

ASJC Scopus subject areas

  • General Computer Science


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