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• W205226640 abstract "Large amounts of spatially referenced data has been aggregated in various application domains such as geographic information systems (GIS), environmental studies, banking and retailing, which motivates the highly demanding field of spatial data mining. So far many optimization problems have been better solved inspired by the foraging behavior of ant colonies. In this paper we propose a novel algorithm for the discovery of spatial trends as one of the most valuable and comprehensive patterns potentially found in a spatial database. Our algorithm applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this knowledge. We apply an effective greedy heuristic combined with the trail intensity being laid by ants using a spatial path. The experimental results on a real banking spatial database show that our method has higher efficiency in performance of the discovery process and in the quality of trend patterns discovered compared to other existing approaches using non-intelligent heuristics." @default.
• W205226640 created "2016-06-24" @default.
• W205226640 creator A5005969937 @default.
• W205226640 creator A5059940092 @default.
• W205226640 date "2006-01-01" @default.
• W205226640 modified "2023-09-26" @default.
• W205226640 title "Mining Spatial Trends by a Colony of Cooperative Ant Agents" @default.
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• W205226640 hasPublicationYear "2006" @default.
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