Intelligent Database Systems Lab

Presenter : YU-TING LU

Authors : Seng Poh Lim and Habibollah Haron

2013. TNNLS

Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data

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Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments

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Motivation• For unstructured data, there is no connectivity

information between data points. As a result,

incorrect shapes will be obtained during the

imaging process.

• 2-D Kohonen maps are limited because they

are unable to cover the whole surface of closed

3-D surface data.

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• closed surface • open surfaces

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Objectives• The aim of this paper is to use KSOM to organize

unstructured data for closed surfaces.

• Enhancements to the KSOM for organizing

unstructured data for closed 3-D surfaces and solving

the problems of 2-D and 3-D KSOM.

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Methodology

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Methodology – Acquiring data• Talus bone data• 5,235 points.

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Methodology – Acquiring data

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Methodology – Initializing parameters

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Methodology – Merging neurons

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Methodology – Merging neurons

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Methodology – Merging neurons

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Methodology – Merging neurons

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Methodology – Detecting neighbors

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Methodology – Generating weights, learning process and producing output

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Experiments - Analysis and validation of images

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Experiments - Analysis and validation of images

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Experiments - Analysis and validation of metric evaluation

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Experiments - Analysis and validation of equations

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Experiments - Analysis and validation of equations

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Quantization errors=0.0001

Quantization errors=0.00007

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Conclusions

• The model solved 2-D KSOM problems by covering

the whole surface of a closed surface and handled

connectivity problems of 3-D KSOM.

• The model also contained fewer quantization errors

compared to 2-D and 3-D KSOM.

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Comments• Advantages

-Fewer quantization errors

• Applications-Self-Organization Map-Organization medical image data