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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Outlines
MotivationObjectivesMethodologyExperimental 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