(untitled) (International Journal of Current Engineering and Technology, 2014)
Association rule discovery from large databases is one of the tedious tasks in data mining. Most of the previous studies on mining association rules are on mining intra 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. Mining intertransaction associations has more challenges on efficient processing than mining intratransaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. In this paper, we reviewed the notion of intertransaction association rule mining given by algorithms such as EH-Apriori and FITI. FTTI generates many unnecessary combinations of items because the set of extended items is much larger than the set of items. Thus, In order to provide efficient approach of g-based intertransaction association rule mining is used, where this group of transactions are following certain constraints. This paper helps to do comparative study of algorithms which will help in analyzing the predictions on stock market data.