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.
|Number of pages||12|
|Journal||Proceedings of the ACM SIGMOD International Conference on Management of Data|
|Publication status||Published - 27 Jul 2004|
|Event||Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2004 - Paris, France|
Duration: 13 Jun 2004 → 18 Jun 2004
ASJC Scopus subject areas
- Computer Science(all)