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

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OutlinesMotivationObjectivesMethodologyExperimental ResultConclusionsComments

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Motivation

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

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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.

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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

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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.

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GSoT algorithm 1

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GSoT algorithm 2

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GSoT algorithm 3

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GSoT algorithm 4

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Experiment-performance

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Experiment-Visual validation

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

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vote

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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.

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Comments

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

• Application- Data visualization Clustering