SOM-based Data Visualization Methods Author:Juha Vesanto Advisor:Dr. Hsu Graduate:ZenJohn Huang IDSL seminar 2002/01/24

2002/1/24IDS Lab seminar2 Outline Motivation Objective Introduction Methods SOM visualization Conclusions

2002/1/24IDS Lab seminar3 Motivation Data mining Complexity or amount of data is prohibitively large for human observation alone An interactive process

2002/1/24IDS Lab seminar4 Objective To give an idea What kind of information can be acquired from different presentations How the SOM can best be utilized in exploratory data visualization

2002/1/24IDS Lab seminar5 SOM(self-organizing map) A neural network algorithm based on unsupervised learning A valuable tool in data mining and KDD Applications in Full-text Financial data analysis Pattern recognition Image analysis Process monitoring Fault diagnosis Introduction

2002/1/24IDS Lab seminar6 SOM Grid 1- or 2-dimension Hexagonal or rectangular

2002/1/24IDS Lab seminar7 SOM (Cont’d) m k := m k + α(t) h ck (t) (x-m k ) α(t) is learning rate h ck (t) is a neighborhood kernel centered on the winner unit c

2002/1/24IDS Lab seminar8 Some Vector quantization Algorithms

2002/1/24IDS Lab seminar9 Some Vector Projection Algorithms

2002/1/24IDS Lab seminar10 Different Between SOM and Other Methods Be not serial combination SOM has a regularly shaped projection grid

2002/1/24IDS Lab seminar11 Disadvantages of the Rigid Grid The grid guides the vector quantization process The axes of the map grid rarely have any clear interpretation The projection implemented by the SOM alone if very crude

2002/1/24IDS Lab seminar12 Projecting Prototype Vectors to a Low Dimension

2002/1/24IDS Lab seminar13 Cluster Structure of the SOM

2002/1/24IDS Lab seminar14 Component Planes and Histograms

2002/1/24IDS Lab seminar15 Cluster Properties |m ik – m jk | / ||m i – m j || k: component i, j: two neighboring map units m i : a prototype vector

2002/1/24IDS Lab seminar16 Contribution of News Paper

2002/1/24IDS Lab seminar17 Component and Reorganized Planes

2002/1/24IDS Lab seminar18 Scatter Plot and Color Map

2002/1/24IDS Lab seminar19 Different Ways to Visualize Data Histograms

2002/1/24IDS Lab seminar20 All and Scandinavian Mills

2002/1/24IDS Lab seminar21 Response Surfaces

2002/1/24IDS Lab seminar22 Quantization Error Plots

2002/1/24IDS Lab seminar23 CCA-like Projection Algorithm

2002/1/24IDS Lab seminar24 Conclusions Bringing the many visualization methods for SOM together Using the software package for Matlab 5 computing environment by Mathworks

2002/1/24IDS Lab seminar25 Future Work Some areas may be discarded as outliers Postprocessing