Document Type
Conference Proceeding
Publication Date
2007
Abstract
This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.
Repository Citation
Feng, M.,
Dong, G.,
Li, J.,
Tan, Y.,
& Wong, L.
(2007). Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed. Lecture Notes in Computer Science, 4426, 489-497.
https://corescholar.libraries.wright.edu/knoesis/292
DOI
10.1007/978-3-540-71701-0_50
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
This paper was presented at 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007.
This paper is the authors' post print.