Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints

Authors:
Vania Bogorny;Joao Valiati;Sandro Camargo;Paulo Engel;Bart Kuijpers;Luis O. Alvares
Affiliations:
Universidade Federal do Rio Grande do Sul (UFRGS), Brazil;Universidade Federal do Rio Grande do Sul (UFRGS), Brazil;Universidade Federal do Rio Grande do Sul (UFRGS), Brazil;Universidade Federal do Rio Grande do Sul (UFRGS), Brazil;Hasselt University and Transnational University of Limburg, Belgium;Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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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.