TY - GEN
T1 - Mining top-K sequential rules
AU - Fournier-Viger, Philippe
AU - Tseng, S.
PY - 2011/12/28
Y1 - 2011/12/28
N2 - Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine-tuning the parameters can be very time-consuming. In this paper, we address this problem by proposing TopSeqRules, an efficient algorithm for mining the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user. Experimental results on real-life datasets show that the algorithm has excellent performance and scalability.
AB - Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine-tuning the parameters can be very time-consuming. In this paper, we address this problem by proposing TopSeqRules, an efficient algorithm for mining the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user. Experimental results on real-life datasets show that the algorithm has excellent performance and scalability.
UR - http://www.scopus.com/inward/record.url?scp=84255160748&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25856-5_14
DO - 10.1007/978-3-642-25856-5_14
M3 - Conference contribution
AN - SCOPUS:84255160748
SN - 9783642258558
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 194
BT - Advanced Data Mining and Applications - 7th International Conference, ADMA 2011, Proceedings
T2 - 7th International Conference on Advanced Data Mining and Applications, ADMA 2011
Y2 - 17 December 2011 through 19 December 2011
ER -