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 KUIJPERS, Bart ALVARES, Luis Otavio |
Issue Date: | 2006 | Publisher: | IEEE | Source: | 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. | Document URI: | http://hdl.handle.net/1942/1409 | Link to publication/dataset: | http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.110 | ISBN: | 978-0-7695-2701-7 | ISI #: | 000245601900082 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2008 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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paperICDM.pdf | Peer-reviewed author version | 789.39 kB | Adobe PDF | View/Open |
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