NDLI: Mining Sequential Pattern Using DF2Ls

Content Provider	IEEE Xplore Digital Library
Author	Xu Yusheng Zhang Lanhui Ma Zhixin Li Lian Xiaoyun Chen Dillon, T.S.
Copyright Year	2008
Description	Author affiliation: Sch. of Inf. Sci. & Technol., Lanzhou Univ., Lanzhou (Xu Yusheng; Zhang Lanhui; Ma Zhixin; Li Lian) \|\| Sch. of Inf. Syst., Curtin Univ., Perth, WA (Xiaoyun Chen; Dillon, T.S.)
Abstract	In this paper, based on SEP and IEP proposed in our previous work, we present two novel pruning strategies, DSEP (dynamic sequence extension pruning) and DIEP (dynamic item extension pruning), which can be used in all Apriori-like sequence mining algorithms or lattice-theoretic approaches. DSEP/DIEP uses DF2Ls (Dynamic Frequent 2-Sequence Lists), which is built by previous enumerations, to prune out infrequent candidate sequences during mining process. With a little more memory overhead, proposed pruning strategies can prune invalidated search space and decrease the total cost of frequency counting effectively. For effectiveness testing reason, we optimize SPAM by using proposed pruning strategies and present the improved algorithm, SPAM+, which uses DSEP and DIEP to prune the search space of SPAM by sharing dynamic frequent 2-sequences lists. A comprehensive performance experiments study shows that SPAM+ outperforms SPAM by a factor of 10 on small datasets and better than 35% to 58% on reasonably large dataset.
Starting Page	600
Ending Page	604
File Size	529942
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Costs Itemsets Databases Unsolicited electronic mail Frequency Electronic mail Space exploration Data mining Testing
Content Type	Text
Resource Type	Article

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