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MBFP generalized association rule mining and classification in traffic volume prediction
Author(s) -
Zhou Huiyu,
Mabu Shingo,
Shimada Kaoru,
Hirasawa Kotaro
Publication year - 2011
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.20682
Subject(s) - association rule learning , data mining , volume (thermodynamics) , association (psychology) , computer science , traffic volume , genetic algorithm , machine learning , artificial intelligence , engineering , transport engineering , philosophy , physics , epistemology , quantum mechanics
Genetic network programming (GNP)‐based time‐related association rules mining method provides a useful mean to investigate future traffic volume of road networks and hence helps us to develop traffic navigation system. Further improvements have been proposed in this paper about the time‐related association rule mining using generalized GNP with multibranches and full‐paths (MBFP) algorithm. For fully utilizing the potential ability of GNP structure, the mechanism of generalized GNP with MBFP is studied. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time‐related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time‐related association rules is described, and experimental results are presented considering a traffic prediction problem. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.