1. Mining Top-n Local Outliers in Large Databases Author: Wen Jin, Anthony K. H. Tung, Jiawei Han Advisor: Dr. Hsu Graduate: Chia- Hsien Wu

2. Outline Motivation Objective Introduction Definition of Local Outlier Algorithm for Finding Top-n Local Outliers Micro-Cluster-Based Algorithm Experimentation Conclusion Opinion

3. Motivation By using a novel notion about LOF for outlier detection has some problems  Resulting a unnecessary computation for every object about its value of LOF  Handling the overlapping data

4. Objective To propose the good performance in finding the most outstanding local outliers We use a meaningful cut-plan to make our algorithms to be useful

5. Introduction There are five general categories about outlier detection. i.e., density-based Using a novel method for top-n local outliers mining that avoid computation of LOF for most objects Using the “Micro-clusters” and bounds notions

6. Definition of Local Outlier DEFINITION 1.(k-distance of p)

7. Definition of Local Outlier (cont.) DEFINITION 2.(k-distance neighborhood of p)

8. Definition of Local Outlier (cont.) DEFINITION 3. (reachability distance of p w.r.t object o)

9. Definition of Local Outlier (cont.)

10. DEFINITION 4.(local reachability density of p) There MinPis is equal to K DEFINITION 5.(local outlier factor of p)

11. Algorithm for Finding Top-n Local Outliers

12. Algorithm for Finding Top-n Local Outliers (cont.)

13. THEOREM 3.2

14. Algorithm for Finding Top-n Local Outliers (cont.)

16. DEFINITION 7.

17. Algorithm for Finding Top-n Local Outliers (cont.) DEFINITION 8.

18. Algorithm for Finding Top-n Local Outliers (cont.) DEFINITION 9.

19. Algorithm for Finding Top-n Local Outliers (cont.)

20. COROLLARY 3.2.

21. Algorithm for Finding Top-n Local Outliers (cont.) DEFINITION 10.(INTERNAL REACHABILITY BOUND OF A MICRO-CLUSTER)

22. Algorithm for Finding Top-n Local Outliers (cont.) DEFINITION 11.(EXTERNAL REACHABILITY BOUND OF TWO MICRO-CLUSTER)

23. Micro-Cluster-Based Algorithm Preprocessing, Computing LOF bound for micro-clusters, Rank top-n local outliers

24. Micro-Cluster-Based Algorithm (cont.) Preprocessing  Load data into CF tree  Fix CF Node and generate micro-clusters  Insert micro-clusters into X-tree

25. Micro-Cluster-Based Algorithm (cont.) Computing LOF Bound for Micro-clusters  Algorithm 1

26. Micro-Cluster-Based Algorithm (cont.) Algorithm 2.

27. Micro-Cluster-Based Algorithm (cont.) Rank Top-n local outliers  Algorithm 3

28. Experimentation

29. Experimentation (cont.)

31. Conclusion We have proposed a novel and efficient method for mining top-n local outliers To find strong and nested local outlier at multiple levels of granularity

32. Opinion How to combine this novel notion and other ways for finding outlier may be a research topic