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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 Presenter : Chang,Chun-ChihAuthors : Miin-Shen Yang a* , Wen-Liang Hung b , De-Hua Chen a2012, FSS
Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments
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.
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.
Methodologycalculate the dissimilarity measure between object 1 and 10
Experiments 5.Cairo 開羅 7.Colombo 巴拉那州 19.Mauritius 摩里斯理
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.
Comments • Advantages - The experimental results also demonstrated that the S-SOM is feasible to treat symbolic data. • Applications - Self-organizing map of Symbolic data