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.
Intelligent Database Systems Lab
Methodology SOM for numeric data
Intelligent Database Systems Lab
Methodology Quantitative type of Ak and Bk
Intelligent Database Systems Lab
Methodology Qualitative type of Ak and Bk
Intelligent Database Systems Lab
Methodologycalculate the dissimilarity measure between object 1 and 10
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
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
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments-Clustering result from our method
Intelligent Database Systems Lab
Experiments-Clustering result of IFCM
Intelligent Database Systems Lab
Experiments-Clustering result from our method
Intelligent Database Systems Lab
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
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.
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