NDLI: Efficient incremental maintenance of frequent patterns with FP-tree

Content Provider	Springer Nature Link
Author	Ma, Xiu Li Tong, Yun Hai Tang, Shi Wei Yang, Dong Qing
Copyright Year	2004
Abstract	Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-Mining (IM), is proposed for maintenance of the frequent patterns when incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth).
Starting Page	876
Ending Page	884
Page Count	9
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	19
Issue Number	6
e-ISSN	18604749
Language	English
Publisher	Science Press
Publisher Date	2008-10-11
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	data mining association rule mining frequent pattern mining incremental mining Computer Science Software Engineering Theory of Computation Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Information Systems Applications (incl. Internet)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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