Growing self-organizing trees for autonomous
hierarchical clustering
Presenter: MIN-CHIEH HSIU
Authors: NHAT-QUANG DOAN∗, HANANE AZZAG, MUSTAPHA LEBBAH
2013 NN
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Outlines
 Motivation
 Objectives
 Methodology
 Experimental Result
 Conclusions
 Comments
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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.
• ApplicationData visualization
Clustering
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