Mining Positive and Negative Patterns for Relevance Feature Discovery

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

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I. M.

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Outlines Motivation Objectives Methodology Experiments Conclusions Comments

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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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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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I. M.Methodology

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Frequency weight

SpecificityWeight

New weight

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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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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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I. M.Mining Algorithms

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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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I. M.Revision of discovered features· Revision of discovered Features

─

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I. M.Revision Algorithms

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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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I. M.Experiments· The well-known term-based methods

─ The Rocchio model

─ BM25

─ SVM

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I. M.Experiments

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Experiments

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I. M.Experiments

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I. M.Experiments

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I. M.Experiments

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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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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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