1. Intelligent Database Systems Lab Presenter : Chang,Chun-Chih Authors : Miin-Shen Yang a*, Wen-Liang Hung b, De-Hua Chen a 2012, FSS Self-organizing map for symbolic data

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation SOM neural network is constructed as a learning algorithm for numeric (vector) data. There is less consideration in a SOM clustering for symbolic data.

4. Intelligent Database Systems Lab Objectives We then use a suppression concept to create a learning rule for neurons. The S-SOM is created for treating symbolic data by embedding the novel structure and the suppression learning rule. This paper can treat symbolic data and a so-called symbolic SOM (S-SOM) is then proposed.

5. Intelligent Database Systems Lab Methodology SOM for numeric data

6. Intelligent Database Systems Lab Methodology Quantitative type of A k and B k

7. Intelligent Database Systems Lab Methodology Qualitative type of A k and B k

8. Intelligent Database Systems Lab Methodology calculate the dissimilarity measure between object 1 and 10

9. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

10. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

11. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

12. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

13. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

14. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

15. Intelligent Database Systems Lab Methodology Calculate the degree of membership Measure Xi and Nj distance Calculatin g the hj(t) Calculating the learning rate

16. Intelligent Database Systems Lab Experiments

17. Intelligent Database Systems Lab Experiments

18. Intelligent Database Systems Lab Experiments

19. Intelligent Database Systems Lab Experiments

20. Intelligent Database Systems Lab Experiments

21. Intelligent Database Systems Lab Experiments

22. Intelligent Database Systems Lab Experiments

23. Intelligent Database Systems Lab Experiments

24. Intelligent Database Systems Lab Experiments - Clustering result from our method

25. Intelligent Database Systems Lab Experiments -Clustering result of IFCM

26. Intelligent Database Systems Lab Experiments -Clustering result from our method

27. Intelligent Database Systems Lab Experiments -37 countries every month temperature

28. Intelligent Database Systems Lab Experiments 5.Cairo 開羅 19. Mauritius 摩里斯理 7.Colombo 巴拉那州

29. Intelligent Database Systems Lab Conclusions The S-SOM can be effective in clustering and also responds information of input symbolic data. The experimental results also demonstrated that the S- SOM is feasible to treat symbolic data.

30. Intelligent Database Systems Lab Comments Advantages - The experimental results also demonstrated that the S-SOM is feasible to treat symbolic data. Applications - Self-organizing map of Symbolic data