Granule Based Inter-transaction Association Rule Mining

Abstract

Association rule mining extends data mining in a number of ways. Han et al. (Han & Kamber, 2006) summarized the features of association mining as single (Agraw et al., 1994) or multi-dimensional (Lee et al., 2006), multiple level (Han et al., 1999), quantitative (Ruckert et al., 2004), sequential pattern (Tzvetkov, P. et al., 2003), constraint based (Pei et al., 2001) etc. However, in line with requirements of knowledge discovered in the real applications, people also divide the association rule mining into intratransaction and intertransaction. Intratransaction was the focus for most traditional approaches which include two stages of frequent itemset mining and rule generation. In frequent itemset mining, Apriori-like (Agraw et al., 1993) and FPT (Han et al., 2000) are two important methods applied in association rule mining. In a transaction database, intratransaction discusses the association of items in the same transaction. For example, if intratransaction association mining is applied in a market basket analysis, the high profit products could be associated with the low profit products in the same transaction. Beyond intratransaction association mining, Lu et al. (2000) proposed intertransaction association mining which is the most widely used in some industry areas. Intertransaction association mining is used to discover patterns among different transactions. In the example of McDonalds, Burger King and KFC, the business association is along locations and also the time dimension as well. Obviously, knowledge discovery in intertransaction association mining is more complicated than intratransaction association mining. To solve intertransaction association mining, Lu et al. (2000) proposed the EH-Apriori and E-Apriori algorithms. However, both algorithms take a significant amount of time to generate large extended itemsets. Tung et al. (2003) proposed the FITI (First Intratransaction Then Intertransaction) algorithm which is adaptive to intertransaction association mining. It focuses on the two stages of mining frequent intertransaction itemsets and rule generation. The experimental results show this method is better for real world applications when compared to the high running time of EH-Apriori. However, finding frequent intertransaction itemsets is still a time consuming process in intertransaction association mining. The FITI algorithm can show the efficiency when the transaction length is very short. If there are many items in each transaction and the threshold is low, the discovery process in frequent intertransaction itemsets could take a very long time. This algorithm considers the intervals of the intertransaction, but does not