DocumentCode :
2566587
Title :
Backward time related association rule mining with database rearrangement in traffic volume prediction
Author :
Zhou, Huiyu ; Mabu, Shingo ; Shimada, Kaoru ; Hirasawa, Kotaro
Author_Institution :
Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitatyushu, Japan
fYear :
2009
fDate :
11-14 Oct. 2009
Firstpage :
1021
Lastpage :
1026
Abstract :
In this paper, backward time related association rule mining using genetic network programming (GNP) with database rearrangement is introduced in order to find time related sequential association from time related databases effectively and efficiently. GNP is a kind of human brain like evolutionary model which represents solutions as directed graph structures. The concept of database rearrangement to better handle association rule extraction from the databases in the traffic volume prediction problems is proposed. The proposed algorithm and experimental results are also included.
Keywords :
data mining; directed graphs; genetic algorithms; traffic engineering computing; association rule extraction; backward time related association rule mining; database rearrangement; directed graph structure; evolutionary model; genetic network programming; traffic volume prediction; Association rules; Cybernetics; Data mining; Economic indicators; Genetics; Real time systems; Spatial databases; Telecommunication traffic; Traffic control; Vehicle dynamics; Backwards; Data Mining; Genetic Network Programming; Time Related; Traffic Volume Prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2009.5346033
Filename :
5346033
Link To Document :
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