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Neural Network Homework Report: Clustering of the Self-Organizing Map. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.11, NO.3, MAY 2000. Professor : Hahn-Ming Lee Student : Hsin-Chung Chen M9315928. OUTLINE. INTRODUCTION CLUSTERING SOM CLUSTERING EXPERIMENTS CONCLUSION.

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Neural network homework report clustering of the self organizing map

Neural Network Homework Report:Clustering of the Self-Organizing Map

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.11, NO.3, MAY 2000

Professor:Hahn-Ming Lee

Student : Hsin-Chung Chen

M9315928


Outline
OUTLINE

  • INTRODUCTION

  • CLUSTERING

  • SOM CLUSTERING

  • EXPERIMENTS

  • CONCLUSION


Introduction
INTRODUCTION

  • DATA mining processes

    • problem definition

    • data acquisition.

    • data preprocessing and survey

    • data modeling

    • evaluation.

    • knowledge deployment.

  • Self-organization map feature:

    • Dimensionality reduction of unsupervised learning

    • Can applied in deal huge amounts of sample

    • The original data set is represented using a smaller set of prototype vectors

    • not to find an optimal clustering but to get good


Clustering
CLUSTERING

  • two main ways approaches

    • hierarchical approaches

      • agglomerative algorithm:

        • bottom-up strategies to build a hierarchical clustering tree

      • divisive algorithm:

        • top-down strategies to build a hierarchical clustering tree

    • partitive approaches

      • k-means

  • optimal clustering is a partitioning

    • minimizes distances within

    • maximizes distances between clusters



Som clustering
SOM CLUSTERING

  • SOM training

    • first to find the best matching unit (BMU)

    • the prototype vectors are updated.


  • The SOM algorithm characteristic

    • applicable to large data sets.

    • The computational complexity scales linearly with the number of data samples

    • it does not require huge amounts of memorythat basically just the prototype vectors and the current training vector .


Experiments
EXPERIMENTS

  • Tools: SOM_ToolBox 2.0 :

  • Data set:clown.dat

    • Data set (“clown.data”) consisted of 2220 2-D samples.

      • cluster with three subclusters (right eye)

      • spherical cluster (left eye)

      • elliptical cluster (nose)

      • nonspherical cluster (U-shaped: mouth)

      • large and sparse cluster (body)

      • noise .(such as black x)


  • Methods and Parameters:

    • Cluster step 1:

      • Training Parameters of the SOM's

      • Map size: 19x17Initial Neighborhood Widths: Rough Phases σ1(0): 10 Fine-Tuning Phases σ2(0): 2learning rates:(The learning rate decreased linearly to zero during the training) Rough Phases : 0.5 Fine-Tuning Phases 0.05

    • Cluster step 2:

      • Method: K-MeansUsing 100 Runs





Complete linkage dendrogram of 323 som map unit
complete linkage dendrogram of 323 SOM Map unit



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