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

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


OutlinesMotivationObjectivesMethodologyExperimental ResultConclusionsComments


Motivation

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


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.


GSoT algorithm

• X = {xi; i = 1, . . . , N} a set of N observations.• List denotes the set that contains all observations.• Each treesi is associated with a weight vector,

denoted by wsi


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.


GSoT algorithm 1


GSoT algorithm 2


GSoT algorithm 3


GSoT algorithm 4


Experiment-performance


Experiment-Visual validation

• Its main advantage is that it provides simultaneous topological and hierarchical organization.


vote





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.


Comments

• This paper presents GSoT improved interactive visualization and clustered efficiency for data.

• Application- Data visualization Clustering

Documents

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