This SpringerBrief provides an overview within data mining of spatiotemporal frequentpattern mining from evolving regions to the perspective of relationshipmodeling among the spatiotemporal objects, frequent pattern miningalgorithms, and data access methodologies for mining algorithms. Whilethe focus of this book is to provide readers insight into the miningalgorithms from evolving regions, the authors also discuss datamanagement for spatiotemporal trajectories, which has becomeincreasingly important with the increasing volume of trajectories. This brief describes state-of-the-art knowledge discoverytechniques to computer science graduatestudents who are interested in spatiotemporal data mining, as well asresearchers/professionals, who deal with advanced spatiotemporal dataanalysis in their fields. These fields include GIS-experts, meteorologists,epidemiologists, neurologists, and solar physicists.
Index Terms
- Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
Recommendations
Closed frequent similar pattern mining
The concept of closed frequent similar pattern mining is introduced.Several lemmas to prune the search space are introduced and proved.A novel closed frequent similar pattern mining algorithm (CFSP-Miner), is proposed.CFSP-Miner is more efficient than ...
Constrained frequent pattern mining: a pattern-growth view
It has been well recognized that frequent pattern mining plays an essential role in many important data mining tasks. However, frequent pattern mining often generates a very large number of patterns and rules, which reduces not only the efficiency but ...
Mining Maximal Frequent Itemsets with Frequent Pattern List
FSKD '07: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01Mining frequent itemsets is a major aspect of association rule research. However, the mining of the complete of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent ...