1. A Test Paradigm for Detecting Changes in Transactional Data Streams Willie Ng and Manoranjan Dash DASFAA 2008

2. Outline Introduction Algorithm for Change Detection Statistical Test Experimental Evaluation Related work Conclusion

3. Introduction A pattern is considered useful if it can be used to help a person to achieve his goal. Unfortunately, traditional association rule mining (ARM) algorithms only consider if an item is absence or present in a transaction. Utility mining-- utility refers to the measuring of how valuable an itemset is. Discoverer & verifier

4. Problem Statement

5. Preliminaries We denote by AHI the set of all high utility itemsets.

6. Two complementary hypotheses The null hypothesis, H 0 – no detectable change The alternative hypothesis, H 1 – there is a detectable change

7. Hoeffding Bound be used to compute a sample size n so that a given statistics on the sample is no more than ε away from the same statistics on the entire database, where ε is a tolerated error.

9. Statistical Test Sample 1: Item A - Utility 30, Item B - Utility 70 Sample 2: Item A - Utility 70, Item B - Utility 30 Paired t-test – mean difference Nonparametric Tests – sign test and Wilcoxon signed-rank test Chi-square Test – the p-value is computed to be 0 with 1 (r-1)*(c-1) degrees of freedom

10. Chi-square Test where O i is the observed count, and E i is the expected count

11. Experimental Evaluation --Test for False Alarm

12. Experimental Evaluation --Test for Changes

13. Experimental Evaluation --Test for Sensitive

14. Related Work For data stream mining, there are three types of data stream mining models: – landmark, – sliding windows – and damped.

15. Conclusions A change detector, ACD, incorporates a statistical tool and is used to detect significant changes in a data stream. It’s not good enough for stream in binary form Outlier