SOM-based Data Visualization Methods

Author: Juha Vesanto

Advisor: Dr. Hsu

Graduate: ZenJohn Huang

IDSL seminar 2002/01/24

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Outline

MotivationObjectiveIntroductionMethodsSOM visualizationConclusions

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Motivation

Data miningComplexity or amount of data is prohibitively large for human observation aloneAn interactive process

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Objective

To give an ideaWhat kind of information can be acquired from different presentations How the SOM can best be utilized in exploratory data visualization

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SOM(self-organizing map)A neural network algorithm based on unsupervised learningA valuable tool in data mining and KDD Applications in

• Full-text• Financial data analysis• Pattern recognition• Image analysis• Process monitoring• Fault diagnosis

Introduction

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SOM

Grid1- or 2-dimensionHexagonal or rectangular

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SOM (Cont’d)

mk := mk + α(t) hck(t) (x-mk)

α(t) is learning ratehck(t) is a neighborhood kernel centered on the winner unit c

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Some Vector quantization Algorithms

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Some Vector Projection Algorithms

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Different Between SOM and Other Methods

Be not serial combinationSOM has a regularly shaped projection grid

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Disadvantages of the Rigid Grid

The grid guides the vector quantization processThe axes of the map grid rarely have any clear interpretationThe projection implemented by the SOM alone if very crude

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Projecting Prototype Vectors to a Low Dimension

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Cluster Structure of the SOM

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Component Planes and Histograms

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Cluster Properties

|mik – mjk| / ||mi – mj||

k: componenti, j: two neighboring map unitsmi: a prototype vector

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Contribution of News Paper

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Component and Reorganized Planes

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Scatter Plot and Color Map

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Different Ways to Visualize Data Histograms

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All and Scandinavian Mills

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Response Surfaces

)1/(1)( 2kk qqg

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Quantization Error Plots

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CCA-like Projection Algorithm

||dx'|| AND maxsteps steps

'1.0':'

)')(1||'||/||||(' ''

3,2,1

while

dxxx

mxmxmxdx

repeat

kkkk

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Conclusions

Bringing the many visualization methods for SOM togetherUsing the software package for Matlab 5 computing environment by Mathworks

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Future Work

Some areas may be discarded as outliersPostprocessing