NDLI: Ordering patterns by combining opinions from multiple sources

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Ordering patterns by combining opinions from multiple sources

Content Provider	ACM Digital Library
Author	Jin, Rong Tan, Pang-Ning
Abstract	Pattern ordering is an important task in data mining because the number of patterns extracted by standard data mining algorithms often exceeds our capacity to manually analyze them. In this paper, we present an effective approach to address the pattern ordering problem by combining the rank information gathered from disparate sources. Although rank aggregation techniques have been developed for applications such as meta-search engines, they are not directly applicable to pattern ordering for two reasons. First, the techniques are mostly supervised, i.e., they require a sufficient amount of labeled data. Second, the objects to be ranked are assumed to be independent and identically distributed (i.i.d), an assumption that seldom holds in pattern ordering. The method proposed in this paper is an adaptation of the original Hedge algorithm, modified to work in an unsupervised learning setting. Techniques for addressing the i.i.d. violation in pattern ordering are also presented. Experimental results demonstrate that our unsupervised Hedge algorithm outperforms many alternative techniques such as those based on weighted average ranking and singular value decomposition.
Starting Page	695
Ending Page	700
Page Count	6
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014142
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rank aggregation Pattern ordering Ensemble learning
Content Type	Text
Resource Type	Article

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