NDLI: An efficient algorithm for mining frequent sequences by a new strategy without support counting

Content Provider	IEEE Xplore Digital Library
Author	Ding-Ying Chiu Yi-Hung Wu Chen, A.L.P.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Ding-Ying Chiu; Yi-Hung Wu; Chen, A.L.P.)
Abstract	Mining sequential patterns in large databases is an important research topic. The main challenge of mining sequential patterns is the high processing cost due to the large amount of data. We propose a new strategy called direct sequence comparison (abbreviated as DISC), which can find frequent sequences without having to compute the support counts of nonfrequent sequences. The main difference between the DISC strategy and the previous works is the way to prune nonfrequent sequences. The previous works are based on the antimonotone property, which prune the nonfrequent sequences according to the frequent sequences with shorter lengths. On the contrary, the DISC strategy prunes the nonfrequent sequences according to the other sequences with the same length. Moreover, we summarize three strategies used in the previous works and design an efficient algorithm called DISC-all to take advantages of all the four strategies. The experimental results show that the DISC-all algorithm outperforms the PrefixSpan algorithm on mining frequent sequences in large databases. In addition, we analyze these strategies to design the dynamic version of our algorithm, which achieves a much better performance.
Sponsorship	Microsoft Res. bea IBM MITRE Sun Microsystems
Starting Page	375
Ending Page	386
File Size	369301
Page Count	12
File Format	PDF
ISBN	0769520650
ISSN	10636382
DOI	10.1109/ICDE.2004.1320012
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Itemsets Algorithm design and analysis Costs Computer science Performance analysis Data mining Data analysis Working environment noise Transaction databases Unsolicited electronic mail
Content Type	Text
Resource Type	Article

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