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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
Intelligent Database Systems Lab
Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
• closed surface • open surfaces
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
Methodology
Intelligent Database Systems Lab
Methodology – Acquiring data• Talus bone data• 5,235 points.
Intelligent Database Systems Lab
Methodology – Acquiring data
Intelligent Database Systems Lab
Methodology – Initializing parameters
Intelligent Database Systems Lab
Methodology – Merging neurons
Intelligent Database Systems Lab
Methodology – Merging neurons
Intelligent Database Systems Lab
Methodology – Merging neurons
Intelligent Database Systems Lab
Methodology – Merging neurons
Intelligent Database Systems Lab
Methodology – Detecting neighbors
Intelligent Database Systems Lab
Methodology – Generating weights, learning process and producing output
Intelligent Database Systems Lab
Experiments - Analysis and validation of images
Intelligent Database Systems Lab
Experiments - Analysis and validation of images
Intelligent Database Systems Lab
Experiments - Analysis and validation of metric evaluation
Intelligent Database Systems Lab
Experiments - Analysis and validation of equations
Intelligent Database Systems Lab
Experiments - Analysis and validation of equations
Intelligent Database Systems Lab
Quantization errors=0.0001
Quantization errors=0.00007
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
Comments• Advantages
-Fewer quantization errors
• Applications-Self-Organization Map-Organization medical image data