Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
Vânia Bogorny, João Francisco Valiati, Sandro da Silva Camargo, Paulo Martins Engel, Bart Kuijpers, Luís Otávio Álvares · Proceedings · 2006
In frequent geographic pattern mining a large amount of patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without associations that are previously known as non- interesting. Geographic dependences are eliminated during the frequent set generation using prior knowledge. After the dependence elimination maximal generalized frequent sets are computed to remove redundant frequent sets. Experimental results show a significant reduction of both the number of frequent sets and the computational time for mining maximal frequent geographic patterns.