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Presenter : Fen-Rou Ciou Authors : M.H. Ghaseminezhad , A. Karami 2011,ASC

A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Presenter : Fen-Rou Ciou Authors : M.H. Ghaseminezhad , A. Karami 2011,ASC. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Fen-Rou Ciou Authors : M.H. Ghaseminezhad , A. Karami 2011,ASC

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  1. A novel self-organizing map (SOM) neural network for discrete groups of data clustering Presenter : Fen-Rou CiouAuthors : M.H. Ghaseminezhad, A. Karami2011,ASC

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • However, no algorithm that can automatically cluster discrete groups of data is presented, and our simulation results show that the classic SOM algorithm cannot cluster discrete data correctly.

  4. Objectives • In this paper present a novel SOM-based algorithm that can automatically cluster discrete groups of data using an unsupervised method.

  5. Methodology

  6. Methodology – First Phase Initialize all weights wij Set Learning iteration number t=0, topological neighborhood d0 , Learning rate ᾳ0 , Total iterations T , Total number of neurons M t < T

  7. Methodology – Second Phase Initialize all weights wij Set Learning iteration number t=0, topological neighborhood d0 , Learning rate ᾳ0 , Total iterations T , Total number of neurons M Batch0,

  8. Methodology – Third Phase

  9. Methodology

  10. Experiments

  11. Experiments

  12. Conclusions • The novel SOM algorithm does a substantially better job of clustering discontinues data as a result of its flexible structure as well as employing the batch learning method.

  13. Comments • Advantages • Applications • SOM

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