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Effective Pattern Discovery for Text Mining
Presenter : Chuang, Kai-Ting
Authors
: Ning Zhong, Yuefeng Li, and Sheng-Tang Wu
2012, TKDE
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
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Motivation
• Many data mining techniques have been proposed
for mining useful patterns in text documents.
• How to effectively use and update discovered
patterns is still an open research issue.
TID
1
2
3
4
5
Items
Bread, Milk
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
Frequent Itemset
E.g.: {Milk, Bread, Diaper}, {Milk}
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Objectives
• This paper presents an innovative and effective
pattern discovery technique which includes the
processes of pattern deploying and pattern evolving.
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Pattern-based ( Phrase-based)
• Low frenquency.
• Misinterpretaion.
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Methodology-Framework
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-Frequent and
Closed Patterns
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-Pattern Taxonomy
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-PDM
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-PDM
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-D-Pattern Mining
Algorithm
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-IPEvolving
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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Methodology-Shuffling
Pattern
Taxonomy Model
Pattern
Deploying
Method
Inner Pattern
Evolution
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The list of methods used for
evaluation
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Baseline Models-Concept-Based
Models
CBM:
CBM Pattern Matching:
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Baseline Models-Term-Based
Mothods
Rocchio:
Prob:
TF-IDF:
BM25:
SVM:
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Comparison of all methods on the
first 50 topics
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Experiment
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Experiment
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Experiment
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Experiment
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Conclusions
• The experimental results show that the proposed
model outperforms not only other pure data mining-
based methods and the concept-based model, but
also term-based state-of-the-art models, such as
BM25 and SVM-based models.
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Comments
• Advantages
– The approach is helpful.
• Applications
– Text mining.
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