Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Future Generation Computer Systems
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient discovery of multilevel spatial association rules using partitions
Information and Software Technology
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nature-Inspired approaches to mining trend patterns in spatial databases
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Hi-index | 0.00 |
Large amounts of spatially referenced data have been aggregated in various application domains such as Geographic Information Systems (GIS), banking and retailing that motivate the highly demanding field of spatial data mining. So far many beneficial optimization solutions have been introduced inspired by the foraging behavior of ant colonies. In this paper a novel algorithm named AntTrend is proposed for efficient discovery of spatial trends. AntTrend applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this valuable knowledge. Ant agents in AntTrend share their individual experience of trend detection by exploiting the phenomenon of stigmergy. Many experiments were run on a real banking spatial database to investigate the properties of the algorithm. The results show that AntTrend has much higher efficiency both in performance of the discovery process and in the quality of patterns discovered compared to non-intelligent methods.