A framework for mining topological patterns in spatio-temporal databases
- 31 October 2005
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 429-436
- https://doi.org/10.1145/1099554.1099680
Abstract
Mining topological patterns in spatial databases has received a lot of attention. However, existing work typically ignores the temporal aspect and suffers from certain efficiency problems. They are not scalable for mining topological patterns in spatio-temporal databases. In this paper, we study the problem for mining topological patterns by incorporating the temporal aspect in the mining process. We introduce a summary-structure that records the instances' count information of a feature in a region within a time window. Using this structure, we design an algorithm, TopologyMiner, to find interesting topological patterns without the need to generate candidates. Experimental results show that TopologyMiner is effective and scalable in finding topological patterns and outperforms Apriori-like algorithm by a few orders of magnitudes.Keywords
This publication has 6 references indexed in Scilit:
- Fast mining of spatial collocationsPublished by Association for Computing Machinery (ACM) ,2004
- Mining confident co-location rules without a support thresholdPublished by Association for Computing Machinery (ACM) ,2003
- Mining frequent neighboring class sets in spatial databasesPublished by Association for Computing Machinery (ACM) ,2001
- Mining frequent patterns by pattern-growthACM SIGKDD Explorations Newsletter, 2000
- CUREPublished by Association for Computing Machinery (ACM) ,1998
- Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its ApplicationsData Mining and Knowledge Discovery, 1998