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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/1409

Title: A Mining Maximal Generalized Frequent Geographic Patterns With Knowledge Constraints
Authors: BOGORNY, Vania
Valiati, J.F.
da Silva Camargo, S
Martins Engel, P
ALVARES, Luis Otavio
Issue Date: 2006
Publisher: IEEE
Citation: Clifton, CW & Zhong, N & Liu, JM & Wah, BW & Wu, XD (Ed.) Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006). p. 813-817.
Series/Report: IEEE International Conference on Data Mining
Abstract: 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.
URI: http://hdl.handle.net/1942/1409
Link to publication: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.110
ISI #: 000245601900082
ISBN: 978-0-7695-2701-7
ISSN: 1550-4786
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2008
Appears in Collections: Research publications

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