Breaking the barrier of transactions
Anthony K. H. Tung, Hongjun Lü, Jiawei Han, Ling Feng · 1999
Most of the previous studies on mining association rules are on mining intro-transaction associations, i.e., the associations among items within the same transaction, where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, etc.In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional intratransaction associations to inter-transaction associations.Mining inter-transaction associations poses more challenges on efficient processing than mining intra-transaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken.In this study, we introduce the notion of inter-transaction association rule, define its measurements: support and confidence, and develop an efficient algorithm, FITI (an acronym for "First Intra Then Inter"), for mining inter-transaction associations.We compare FITI with EH-Apriori, the best algorithm in our previous proposal, and demonstrate a substantial performance gain of FITI over EH- Apriori.