Backward time related association rule mining with database rearrangement in traffic volume prediction
Backward time related association rule mining with database rearrangement in traffic volume prediction
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DOI：
10.1109/icsmc.2009.5346033
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Huiyu Zhou;S. Mabu;K. Shimada;K. Hirasawa
中科院分区：
文献类型：
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作者：
Huiyu Zhou;S. Mabu;K. Shimada;K. Hirasawa
为了从时间相关的数据库中高效地发现时间相关的顺序关联，本文引入了基于数据库重排的遗传网络规划（GNP）倒向时间相关关联规则挖掘方法。GNP是一种类似人脑的进化模型，它将解表示为有向图结构。提出了数据库重排的概念，以更好地处理从数据库中提取关联规则的交通量预测问题。最后给出了算法和实验结果。
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.