1. 1 A methodology for dynamic data mining based on fuzzy clustering Source: Fuzzy Sets and Systems Volume: 150, Issue: 2, March 1, 2005, pp. 267-284 Authors: Fernando Crespo 、 Richard Weber Speaker: 黃琬淑 (Wan-Shu Huang) Date: 2005/12/22

2. 2 Outline Introduction Dynamic data mining using fuzzy clustering Application Conclusions and comment

3. 3 Introduction(1/3) Clustering technique is to group similar objects into the same classes Keep applying data mining system in a changing environment 1.Neglects changes and without any updating 2.A new system is developed 3.Update of the classifier

4. 4 Introduction(2/3) Propose a methodology follow strategy 3  First identify the need for a system’s update by applying it to new data.  Second perform the update by using efficiently the previous system.

5. 5 Introduction(3/3) Taxonomy of dynamic data mining for clustering Hierarchical clustering e.g. CHAMELEON Partitional clustering e.g. c-means and fuzzy c-means

6. 6 Dynamic data mining using fuzzy clustering(1/11) Possible changes of the classifier’s structure  Creation of new classes  Elimination of classes  Movement of classes

7. 7 Dynamic data mining using fuzzy clustering(2/11)

8. 8 Dynamic data mining using fuzzy clustering(3/11) Step 1 Identify objects that represent changes

9. 9 Dynamic data mining using fuzzy clustering(4/11) Condition 1 ： not classified well by the existing classifier Condition 2 ： far away from the current classes

10. 10 Dynamic data mining using fuzzy clustering(5/11) Based on these two conditions If, process with step 3.1 else go to step 2

11. 11 Dynamic data mining using fuzzy clustering(6/11) Step2 Determine changes of class structure Above β create new classes (step 3.2) else just move the existing classes (step3.1)

12. 12 Dynamic data mining using fuzzy clustering(7/11) Step3.1 Move classes

13. 13 Dynamic data mining using fuzzy clustering(8/11) Step 3.2 Create classes C 越大， E 越小， L(c ）越小， L 越大 C 越小， E 越大， L(c ）越大， L 越小

14. 14 Dynamic data mining using fuzzy clustering(9/11)

15. 15 Dynamic data mining using fuzzy clustering(10/11) Step 4 Identify trajectories of classes First set counter is Created class i in cycle t-1 ， set counter ： =1 Class I is the result of moving a class j in cycle t-1, set counter ：

16. 16 Dynamic data mining using fuzzy clustering(11/11) Step 5 Eliminate unchanged classes  A class has to be eliminated if it did not receive new objects for a long period.

17. 17 Application of the proposed methodology(1/8) 500 objects for each of the four classes Shows the initial data set (0,15)(8,35) (15,0)(15,20)

18. 18 Application of the proposed methodology(2/8) Apply fuzzy c-means with c=4 and m=2 Presents the respective cluster solution

19. 19 Application of the proposed methodology(3/8) In the first cycle 600 new objects arrive

20. 20 Application of the proposed methodology(4/8) Results after fist cycle

21. 21 Application of the proposed methodology(5/8) In the second cycle 500 new objects arrive

22. 22 Application of the proposed methodology(6/8) Results after second cycle

23. 23 Application of the proposed methodology(7/8) In the third cycle 600 new objects arrive

24. 24 Application of the proposed methodology (8/8) Results after third cycle

25. 25 Conclusions and comment Presented a methodology Used fuzzy c-means Provide updated class structures Analyzing changes in application domain The parameters of set is a question