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