Backward time related association rule mining with database rearrangement in traffic volume prediction

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.