A NEW FRAMEWORK OF MINING ASSOCIATION RULES WITH TIME-WINDOWS ON REAL-TIME TRANSACTION DATABASE
Yiyong Xiao, Renqian Zhang, Ikou Kaku · International journal of innovative computing, information & control · 2011
.jp Abstract. This paper dedicates efforts to discover the part-time association rules in real-time transactional database by extending the traditional minsup-minconf based frame- work to a new one { the minsup, minconf and minwin based framework. We propose a more general form for association rule, i.e., the Association Rule with Time-Windows (ARTW), to properly integrate the temporal association rules together with the normal ones. New notions like Frequent Itemset with Time-Windows (FITW) are also dened, and an Apriori-like algorithm, named TW-Apriori, is developed to fast generate the FITWs. Computational experiments are conducted on two datasets { a synthetic dataset and a real database. Both experiments show that large number of ARTWs ignored pre- viously can be discovered under the new framework; many of them are even very strong rules and valuable for market decisions. The efficiency of the proposed TW-Apriori al- gorithm is also proven feasible since it cannish the calculation within one minute and the length of the calculation time is nearly proportional to the number of ARTWs found.