Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Zhou:2008:cec,
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author = "Huiyu Zhou and Wei Wei and Kaoru Shimada and
Shingo Mabu and Kotaro Hirasawa",
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title = "Time Related Association Rules Mining with Attributes
Accumulation Mechanism and its Application to Traffic
Prediction",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "305--311",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0092.pdf",
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DOI = "doi:10.1109/CEC.2008.4630815",
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abstract = "We propose a method of association rule mining using
Genetic Network Programming (GNP) with time series
processing mechanism and attribute accumulation
mechanism in order to find time related sequence rules
efficiently in association rule extraction systems. We
suppose that, the database consists of a large number
of attributes based on time series. In order to deal
with databases which have a large number of attributes,
GNP individual accumulates better attributes in it
gradually round by round, and the rules of each round
are stored in the Small Rule Pool using hash method,
and the new rules will be finally stored in the Big
Rule Pool. The aim of this paper is to better handle
association rule extraction of the database in many
time-related applications especially in the traffic
prediction problem. In this paper, the algorithm
capable of finding the important time related
association rules is described and experimental results
considering a traffic prediction problem are
presented.",
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keywords = "genetic algorithms, genetic programming",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Huiyu Zhou
Wei Wei
Kaoru Shimada
Shingo Mabu
Kotaro Hirasawa
Citations