Mining Association Rules from Stars Eric Ka Ka Ng, Ada Wai-Chee Fu, and Ke Wang   View Paper (PDF)   Return to Main-Track Regular Papers Abstract Association rule mining is an important data mining problem. It is found to be useful for conventional relational data. However, previous work had mostly targeted on mining a single table. In real life, a database is typically made up of multiple table and one important case is where some of the tables form a star schema. That tables typically correspond to entity sets and joining the tables in a star schema gives relationship amoung entity sets which can be very interesting information. Hence mining on the join result is an important problem. Based on characteristics of the star schema we propose an efficient algorithm for mining association rules on the join result but without actually performing the join opertation. We show that this approach can significantly out-perform the join-then-mine approach even when the latter adopts a fastest known mining algorithm. DiSC'03 © 2003 Association for Computing Machinery