Effective Pattern Discovery for Text Mining PRESENTER CHUANG

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 n Motivation n Objectives n Methodology n Experiments n Conclusions n Comments Intelligent Database Systems Lab

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. Frequent Itemset E. g. : {Milk, Bread, Diaper}, {Milk} Intelligent Database Systems Lab

Objectives • This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving. Intelligent Database Systems Lab

Pattern-based ( Phrase-based) • Low frenquency. • Misinterpretaion. Intelligent Database Systems Lab

Methodology-Framework Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-Frequent and Closed Patterns Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-PDM Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-PDM Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-D-Pattern Mining Algorithm Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-IPEvolving Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

Methodology-Shuffling Pattern Taxonomy Model Pattern Deploying Method Inner Pattern Evolution Intelligent Database Systems Lab

The list of methods used for evaluation Intelligent Database Systems Lab

Baseline Models-Concept-Based Models CBM: CBM Pattern Matching: Intelligent Database Systems Lab

Baseline Models-Term-Based Mothods Rocchio: Prob: TF-IDF: BM 25: SVM: Intelligent Database Systems Lab

Comparison of all methods on the first 50 topics Intelligent Database Systems Lab

Experiment Intelligent Database Systems Lab

Experiment Intelligent Database Systems Lab

Experiment Intelligent Database Systems Lab

Experiment Intelligent Database Systems Lab

Conclusions • The experimental results show that the proposed model outperforms not only other pure data miningbased methods and the concept-based model, but also term-based state-of-the-art models, such as BM 25 and SVM-based models. Intelligent Database Systems Lab

Comments • Advantages – The approach is helpful. • Applications – Text mining. Intelligent Database Systems Lab