Self-organizing map for symbolic data
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Intelligent Database Systems Presenter : Chang,Chun-Chih Authors : Miin-Shen Yang a* , Wen-Liang Hung , De-Hua Chen a 2012, FSS Self-organizing map for symbolic data
description
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. - PowerPoint PPT Presentation
Transcript of Self-organizing map for symbolic data
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
Intelligent Database Systems Lab
Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
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.
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.
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Methodology SOM for numeric data
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Methodology Quantitative type of Ak and Bk
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Methodology Qualitative type of Ak and Bk
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Methodologycalculate the dissimilarity measure between object 1 and 10
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Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Methodology Calculate the degree of
membership
Measure Xi and Nj
distance
Calculating the hj(t)
Calculating the learning
rate
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
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Experiments
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Experiments
Intelligent Database Systems Lab
Experiments
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Experiments-Clustering result from our method
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Experiments-Clustering result of IFCM
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Experiments-Clustering result from our method
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Experiments-37 countries every month temperature
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Experiments
5.Cairo 開羅
19.Mauritius 摩里斯理
7.Colombo 巴拉那州
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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.
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Comments
• Advantages - The experimental results also demonstrated that the S-SOM is feasible to treat symbolic data. • Applications - Self-organizing map of Symbolic data
