國立雲林科技大學
National Yunlin University of Science and Technology
The Evolving Tree—Analysis and
Applications
Advisor : Dr. Hsu
Presenter : Zih-Hui Lin
Author
:Jussi Pakkanen, Jukka Iivarinen, and Erkki Oja,
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Outline
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Motivation
Objective
ETree
ETree-Analysis
Experiments
Conclusions
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Motivation
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Some of its intrinsic features make it unsuitable for
analyzing very large scale problems.
This converts complexity control from a global
problem to a local one which is simpler.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Objective
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We have analyzed and compared the ETree
against many different systems.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree
Training data Xi
hop
1)Find the BMU using the search tree
2) Update the leaf node locations using the SOM training
formulas substituting tree distance for grid distance.
BMU
3) Increment the BMU’s hit counter.
4) If the counter reaches the splitting threshold, split the node.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree- How to controlling the growth
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree- Removing Layers
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One beneficial feature of most neural networks is
graceful degradation.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree
better Search for the BMU
At every layer we keep the n best subbranches instead
of only one.
Regular BMU
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree
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Child Node Initialization
The first perturbs the child nodes randomly.
The second one is based on principal component analysis (PCA).
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
ETree-
Optimizing the Leaf Node Locations
1)First, we map all training vectors to leaf nodes using
the established BMU search.
2)Then we move the leaf nodes to the center of mass
of their respective data vectors.
Large dataset
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Visualization experiments
data
vector
SOM
ETree
Leaf nodes
K-means
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Quality of clustering
without the search tree
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Conclusions
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ETree’s performance is quite close to classical,
nonhierarchical algorithms but it is noticeably
faster,
ETree makes implementation and application
easier.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
My opinion
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Advantage:
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Disadvantage:
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…
…
Apply
clustering, classification, large dataset
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Intelligent Database Systems Lab