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Self-organizing map for symbolic data

Self-organizing map for symbolic data. Presenter : Chang,Chun-Chih Authors : Miin-Shen Yang a* , Wen-Liang Hung b , De- Hua Chen a 2012, FSS. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Self-organizing map for symbolic data

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  1. Self-organizing map for symbolic data Presenter : Chang,Chun-ChihAuthors : Miin-Shen Yang a* , Wen-Liang Hung b , De-Hua Chen a2012, FSS

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. 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. 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 symbolicSOM (S-SOM) is then proposed.

  5. Methodology SOM for numeric data

  6. MethodologyQuantitative type of Ak and Bk

  7. MethodologyQualitative type of Ak and Bk

  8. Methodologycalculate the dissimilarity measure between object 1 and 10

  9. Methodology

  10. Methodology

  11. Methodology

  12. Methodology

  13. Methodology

  14. Methodology

  15. Methodology

  16. Experiments

  17. Experiments

  18. Experiments

  19. Experiments

  20. Experiments

  21. Experiments

  22. Experiments

  23. Experiments

  24. Experiments-Clustering result from our method

  25. Experiments-Clustering result of IFCM

  26. Experiments-Clustering result from our method

  27. Experiments-37 countries every month temperature

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

  29. 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. Comments • Advantages - The experimental results also demonstrated that the S-SOM is feasible to treat symbolic data. • Applications - Self-organizing map of Symbolic data

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