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Sixth IEEE International Conference on Data Mining (ICDM'06)
Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
Hong Kong
December 18-December 22
ISBN: 0-7695-2701-9
ASCII Text
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Vania Bogorny, Joao Valiati, Sandro Camargo, Paulo Engel, Bart Kuijpers, Luis O. Alvares, "Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints," Data Mining, IEEE International Conference on, pp. 813-817, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDM.2006.110,
author = {Vania Bogorny and Joao Valiati and Sandro Camargo and Paulo Engel and Bart Kuijpers and Luis O. Alvares},
title = {Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2006},
issn = {1550-4786},
pages = {813-817},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.110},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
SN - 1550-4786
SP813
EP817
A1 - Vania Bogorny,
A1 - Joao Valiati,
A1 - Sandro Camargo,
A1 - Paulo Engel,
A1 - Bart Kuijpers,
A1 - Luis O. Alvares,
PY - 2006
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Vania Bogorny, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
Joao Valiati, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
Sandro Camargo, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
Paulo Engel, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
Bart Kuijpers, Hasselt University and Transnational University of Limburg, Belgium
Luis O. Alvares, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil
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.
Citation:
Vania Bogorny, Joao Valiati, Sandro Camargo, Paulo Engel, Bart Kuijpers, Luis O. Alvares, "Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints," icdm, pp.813-817, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006
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