1. Intelligent Database Systems Lab Presenter: MIN-CHIEH HSIU Authors: NHAT-QUANG DOAN ∗, HANANE AZZAG, MUSTAPHA LEBBAH 2013 NN Growing self-organizing trees for autonomous hierarchical clustering

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experimental Result Conclusions Comments

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

4. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab GSoT algorithm X = {xi; i = 1,..., N} a set of N observations. List denotes the set that contains all observations. Each tree si is associated with a weight vector, denoted by w si

6. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab GSoT algorithm 1

8. Intelligent Database Systems Lab GSoT algorithm 2

9. Intelligent Database Systems Lab GSoT algorithm 3

10. Intelligent Database Systems Lab GSoT algorithm 4

11. Intelligent Database Systems Lab Experiment-performance

12. Intelligent Database Systems Lab Experiment-Visual validation Its main advantage is that it provides simultaneous topological and hierarchical organization.

13. Intelligent Database Systems Lab vote

14. Intelligent Database Systems Lab

17. 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.

18. Intelligent Database Systems Lab Comments This paper presents GSoT improved interactive visualization and clustered efficiency for data. Application- Data visualization Clustering