NDLI: Extracting redundancy-aware top-k patterns

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Extracting redundancy-aware top-k patterns

Content Provider	ACM Digital Library
Author	Cheng, Hong Xin, Dong Yan, Xifeng Han, Jiawei
Abstract	Observed in many applications, there is a potential need of extracting a small set of frequent patterns having not only high significance but also low redundancy. The significance is usually defined by the context of applications. Previous studies have been concentrating on how to compute top-k significant patterns or how to remove redundancy among patterns separately. There is limited work on finding those top-k patterns which demonstrate high-significance and low-redundancy simultaneously.In this paper, we study the problem of extracting redundancy-aware top-k patterns from a large collection of frequent patterns. We first examine the evaluation functions for measuring the combined significance of a pattern set and propose the MMS (Maximal Marginal Significance) as the problem formulation. The problem is known as NP-hard. We further present a greedy algorithm which approximates the optimal solution with performance bound O(log k) (with conditions on redundancy), where k is the number of reported patterns. The direct usage of redundancy-aware top-k patterns is illustrated through two real applications: disk block prefetch and document theme extraction. Our method can also be applied to processing redundancy-aware top-k queries in traditional database.
Starting Page	444
Ending Page	453
Page Count	10
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150452
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Redundancy Significance Pattern extraction
Content Type	Text
Resource Type	Article

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