Mining Multi-Level Rules with Recurrent Items using FP'-Tree
Association rule mining has received broad research in the academic and wide application in the real world. As a result, many variations exist and one such variant is the mining of multi-level rules. The mining of multi-level rules has proved to be useful in discovering important knowledge that conventional algorithms such as Apriori, SETM, DIC etc., miss. However, existing techniques for mining multi-level rules have failed to take into account the recurrence relationship that can occur in a transaction during the translation of an atomic item to a higher level representation. As a result, rules containing recurrent items go unnoticed. In this paper, we consider the notion of `quantity' to an item, and present an algorithm based on an extension of the FP-Tree to find association rules with recurrent items at multiple concept levels.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Third International Conference on Information Communications and Signal Processing (ICICS 2001)
City or Country
Singapore, Oct 15-18
ONG, Kok-Leong; NG, Wee-Keong; and LIM, Ee Peng.
Mining Multi-Level Rules with Recurrent Items using FP'-Tree. (2001). Third International Conference on Information Communications and Signal Processing (ICICS 2001). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/904