Efficient incremental maintenance of frequent patterns with FP-tree

Authors:
Xiu-Li Ma;Yun-Hai Tong;Shi-Wei Tang;Dong-Qing Yang
Affiliations:
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, P.R. China and National Laboratory on Machine Perception, Peking University, Beijing 100871, P.R. China;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, P.R. China and National Laboratory on Machine Perception, Peking University, Beijing 100871, P.R. China;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, P.R. China and National Laboratory on Machine Perception, Peking University, Beijing 100871, P.R. China;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, P.R. China
Venue:
Journal of Computer Science and Technology
Year:
2004

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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 new 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).