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Presenter : MIN-CHIEH HSIU Authors: Nhat-Quang Doan∗, Hanane Azzag , Mustapha Lebbah 2013 NN

Growing self-organizing trees for autonomous hierarchical clustering. Presenter : MIN-CHIEH HSIU Authors: Nhat-Quang Doan∗, Hanane Azzag , Mustapha Lebbah 2013 NN. Outlines. Motivation Objectives Methodology Experimental Result Conclusions Comments. Motivation.

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Presenter : MIN-CHIEH HSIU Authors: Nhat-Quang Doan∗, Hanane Azzag , Mustapha Lebbah 2013 NN

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  1. Growing self-organizing trees for autonomous hierarchical clustering Presenter: MIN-CHIEH HSIUAuthors: Nhat-Quang Doan∗, HananeAzzag, Mustapha Lebbah2013 NN

  2. Outlines • Motivation • Objectives • Methodology • Experimental Result • Conclusions • Comments

  3. Motivation • Discovering the inherent structure and its uses in large datasets has become a major challenge for data mining applications.

  4. Objectives • This authors aim to build an autonomous hierarchical clustering system using the self-organization concept that runs autonomously without using parameters. • GSoT: Growing Self-organizing Trees.

  5. GSoT algorithm • X = {xi; i = 1, . . . , N} a set of N observations. • List denotes the set that contains all observations. • Each treesiis associated with a weight vector, denoted by wsi

  6. GSoT algorithm function status (xi) • initial: the default status before training. • connected: node xi is currently connected to another node. • disconnected: node xi was connected at least once but gets disconnected.

  7. GSoTalgorithm 1

  8. GSoT algorithm 2

  9. GSoTalgorithm 3

  10. GSoTalgorithm 4

  11. Experiment-performance

  12. Experiment-Visual validation • Its main advantage is that it provides simultaneous topological and hierarchical organization.

  13. vote

  14. Conclusions • This paper presents a new approach that allows for simultaneous clustering and visualization. • The tree structure allows the user to understand and analyze large amounts of data in an explorative manner.

  15. Comments • This paper presents GSoTimproved interactive visualization and clustered efficiency for data. • Application- Data visualization Clustering

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