Neural network homework report clustering of the self organizing map
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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

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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


Clustering cont

CLUSTERING(cont.)


Som clustering

SOM CLUSTERING

  • SOM training

    • first to find the best matching unit (BMU)

    • the prototype vectors are updated.


Neural network homework report clustering of the self organizing map

  • 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)


Neural network homework report clustering of the self organizing map

  • 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


Experimental results

Experimental Results


Single linkage dendrogram of 323 som map unit

single linkage dendrogram of 323 SOM Map unit


Som map average linkage dendrogram of 323 som map unit

SOM Map average linkage dendrogram of 323 SOM Map unit


Complete linkage dendrogram of 323 som map unit

complete linkage dendrogram of 323 SOM Map unit


Neural network homework report clustering of the self organizing map

Conclusion


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