This paper introduces the problem of data mining association rules. We adopt the iterative method to enlarge the size of the item set gradually and describe the hierarchical algorithm in detail. The hierarchical algorithm produces a larger provisional sets based on the obtained frequent item sets and make sure that those provisional sets which will never be frequent item set are ignored under the premise of the known information. Finally, an improving algorithm which is to combine the last several procedures of iteration into a single scan of the database D. Mainly because that the more backwards the iterative processes approach the end, the less the provisional sets are there.

Date Published: 12 March 2002
PDF: 6 pages
Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460215

Published in SPIE Proceedings Vol. 4730:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV
Belur V. Dasarathy, Editor(s)