It has been widely accepted that association rules mining, the task of searching for correlations between items in a database, can discover useful rules in stock analysis. Among many techniques of data mining, association rule mining is widely accepted for finding co-relation between items in databases, which can be effectively used for forecasting the trends in stocks, future market analysis and setting strategies for super markets etc. Previous studies on association rule mining mainly emphasize on mining intratransaction associations. "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than IJCA must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, needs an acknowledgement to IJCA." Intertransaction association rule mining is used to discover patterns between different transactions. It breaks the scope of association rule mining on the same transaction. Currently the FITI algorithm is the state of the art in intertransaction association rule mining. However, the FTTI introduces many unneeded combinations of items because the set of extended items is much larger than the set of items. Thus, we propose an alternative approach of granule based intertransaction association rule mining, where a granule is a group of transactions that meet a certain constraint. The experimental results show that this approach is promising in real-world industry.

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multidimensional databases