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Mining Positive and Negative Patterns for Relevance Feature Discovery. Presenter : Cheng- Hui Chen Author : Yuefeng Li, Abdulmohsen Algarni , Ning Zhong KDD 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
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Mining Positive and Negative Patterns for Relevance Feature Discovery
Presenter : Cheng-Hui Chen Author : Yuefeng Li, Abdulmohsen Algarni, Ning Zhong
KDD 2010
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Outlines Motivation Objectives Methodology Experiments Conclusions Comments
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation· Over the years, people have often held the
hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis.
· Many text mining only consider term’s distributions.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objectives· The innovative technique presented in paper
makes a breakthrough for this difficulty.· To purpose consider both term’s distributions
and their specificities when we use them for text mining and classification.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Frequency weight
SpecificityWeight
New weight
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Definitions· Frequent pattern
─ Absolute support:─ Relative support :─ A termset X is called, if supa (or supr) >= min_sup
· Closed pattern─ ─ Cls (X) = termset (coverset (X))─ A termset X is called, if and only if X = Cls (X) ─ , for all pattern X1 X
· Closed sequential pattern
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.The deploying method· To improve the efficiency of the pattern taxonomy
mining (PTM), an algorithm, SPMining(D+; min_sup).─ For a given term t, its support (or called weight) in
discovered patterns can be described as follow:
─ the following rank will be assigned to every incoming document d to decide its relevance.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Mining Algorithms
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Specificity of low-level features· We define the specificity of a given term t in
the training set D = D+ ∪ D- as follows:
─
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Revision of discovered features· Revision of discovered Features
─
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Revision Algorithms
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments· Data
─ This research uses Reuters Corpus Volume1 (RCV1) and the 50 assessor topics to evaluate the proposed model.
· Compare ─ The up-to date pattern mining─ The well-known term-based method
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments· The well-known term-based methods
─ The Rocchio model
─ BM25
─ SVM
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Experiments
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusions· Compared with the state-of-the-art models, the
experiments on RCV1 and TREC topics demonstrate that the effectiveness of relevance feature discovery can be significantly improved by the proposed approach.
· This paper recommends to classify low-level terms into three categories in order to largely improve the performance of the revision.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments· Advantages
─ The effectiveness of relevance feature discovery can be significantly improved by the proposed approach.
· Drawback─ …
· Applications─ Text mining─ Classification
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