TY - GEN
T1 - Mining top-k non-redundant association rules
AU - Fournier-Viger, Philippe
AU - Tseng, S.
PY - 2012
Y1 - 2012
N2 - Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both of them at the same time by proposing an approximate algorithm named TNR for mining top-k non redundant association rules.
AB - Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both of them at the same time by proposing an approximate algorithm named TNR for mining top-k non redundant association rules.
KW - algorithm
KW - association rules
KW - non-redundant rules
KW - top-k
UR - http://www.scopus.com/inward/record.url?scp=84870937270&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34624-8_4
DO - 10.1007/978-3-642-34624-8_4
M3 - Conference contribution
AN - SCOPUS:84870937270
SN - 9783642346231
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 31
EP - 40
BT - Foundations of Intelligent Systems - 20th International Symposium, ISMIS 2012, Proceedings
T2 - 20th International Symposium on Methodologies for Intelligent Systems, ISMIS 2012
Y2 - 4 December 2012 through 7 December 2012
ER -