In order to efficiently trace the changes of association rules over an online data stream, this paper proposes a method of generating association rules directly over the changing set of currently frequent itemsets. While all of the currently frequent itemsets in an online data stream are monitored by the estDec method, all the association rules of every frequent itemset in the prefix tree of the estDec method are generated by the proposed method in this paper. For this purpose, a traversal stack is introduced to efficiently enumerate all association rules in the prefix tree. This online implementation can avoid the drawbacks of the conventional two-step approach. In addition, the prefix tree itself can be utilized as an index structure for finding the current support of the antecedent of an association rule. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.
Bibliographical noteFunding Information:
This work was financially supported by the Korea Science and Engineering Foundation (KOSEF) through the National Research Lab. Program funded by the Ministry of Science and Technology (No.R0A-2006-000-10225-0) and was partially supported by the Ministry of Education Human Resources Development (MOE), the Ministry of Commerce, Industry and Energy (MOCIE) and the Ministry of Labor (MOLAB) through the fostering project of the Lab of Excellency.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications