NDLI: A general mining method for incremental updation in large databases

Content Provider	IEEE Xplore Digital Library
Author	Wan-Jui Lee Shie-Jue Lee
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan (Wan-Jui Lee; Shie-Jue Lee)
Abstract	The database used for knowledge discovery is dynamic in nature. Data may be updated and new transactions may be added over time. As a result, the knowledge discovered from such databases is also dynamic. Incremental mining techniques have been developed to speed up the knowledge discovery process by avoiding re-learning of rules from the old data. To maintain the large itemsets against the updated database, we develop an approach named Negative Border using Sliding-Window Filtering (NB-SWF) which adopts the idea of the negative border and the sliding-window filtering algorithm. Negative border can help reduce the number of scans over the original database and the sliding-window filtering algorithm is to discover new itemsets in the updated database. By integrating the sliding-window filtering algorithm with the negative border, a lot of effort in the re-computation of negative border can be saved, and the minimal candidate set of large itemsets and negative border in the updated database can be obtained efficiently. Simulation results have shown that the NB-SWF runs faster than other incremental mining techniques, especially when there are few new large itemsets in the updated database.
Sponsorship	Syst., Man & Cybernetics Soc. IEEE
Starting Page	1423
Ending Page	1428
File Size	417770
Page Count	6
File Format	PDF
ISBN	0780379527
ISSN	1062922X
DOI	10.1109/ICSMC.2003.1244612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Itemsets Transaction databases Data mining Filtering algorithms Association rules Councils Availability Partitioning algorithms Computational modeling
Content Type	Text
Resource Type	Article

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