TY - GEN
T1 - CCMine
T2 - 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
AU - Kim, Won Young
AU - Lee, Young Koo
AU - Han, Jiawei
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2004.
PY - 2004
Y1 - 2004
N2 - Correlated pattern mining has become increasingly important recently as an alternative or an augmentation of association rule mining. Though correlated pattern mining discloses the correlation relationships among data objects and reduces significantly the number of patterns produced by the association mining, it still generates quite a large number of patterns. In this paper, we propose closed correlated pattern mining to reduce the number of the correlated patterns produced without information loss. We first propose a new notion of the confidence-closed correlated patterns, and then present an efficient algorithm, called CCMine, for mining those patterns. Our performance study shows that confidence-closed pattern mining reduces the number of patterns by at least an order of magnitude. It also shows that CCMine outperforms a simple method making use of the the traditional closed pattern miner. We conclude that confidence-closed pattern mining is a valuable approach to condensing correlated patterns.
AB - Correlated pattern mining has become increasingly important recently as an alternative or an augmentation of association rule mining. Though correlated pattern mining discloses the correlation relationships among data objects and reduces significantly the number of patterns produced by the association mining, it still generates quite a large number of patterns. In this paper, we propose closed correlated pattern mining to reduce the number of the correlated patterns produced without information loss. We first propose a new notion of the confidence-closed correlated patterns, and then present an efficient algorithm, called CCMine, for mining those patterns. Our performance study shows that confidence-closed pattern mining reduces the number of patterns by at least an order of magnitude. It also shows that CCMine outperforms a simple method making use of the the traditional closed pattern miner. We conclude that confidence-closed pattern mining is a valuable approach to condensing correlated patterns.
UR - http://www.scopus.com/inward/record.url?scp=7444228742&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=7444228742&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-24775-3_68
DO - 10.1007/978-3-540-24775-3_68
M3 - Conference contribution
AN - SCOPUS:7444228742
SN - 354022064X
SN - 9783540220640
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 569
EP - 579
BT - Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
A2 - Dai, Honghua
A2 - Srikant, Ramakrishnan
A2 - Zhang, Chengqi
PB - Springer
Y2 - 26 May 2004 through 28 May 2004
ER -