1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Direct mining of discriminative patterns for classifying uncertain data Presenter : Cheng-Hui Chen Author : Chuancong Gao, Jiayong Wang KDD 2010

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation  A test instance is classified later using classifier trained based on the mined patterns., however, it takes a great amount of running time in both pattern mining and feature selection.  The uncertainty is usually caused by noise, measurement limits, or other possible factors. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives  We propose a novel algorithm uHARMONY which mines discriminative patterns directly and effectively from uncertain data as classification features/rules, to help train either SVM or rule-based classifier. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Framework 5 Uncertain data Certain data Certain data Expected confidence Expected confidence Upper bound > Conf max Stop No Yes Sup x >Sup min Yes Add pattern SVM Rule-base classifier

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  The uncertain data model 6 = 0.1+ 0.8  Frequent itemset mining  Expected Confidence

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Efficient computation expected confidence 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Upper bound of expected confidence 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Mining algorithm uHARMONY 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Classification Algorithm  SVM classifier  Rule-base classifier 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 14

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 15

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Scalability test 16

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  To propose a novel algorithm to solve the classification problem on uncertain categorical data.  The algorithm outperforms the state-of-the-art algorithms significantly with 4% to 10% improvements on average in terms of accuracy. 17

18. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments  Advantages  Applications ─ Classification on uncertain data 18