advisor : dr. hsu presenter : ai-chen liao authors : yiu-ming cheung
DESCRIPTION
On R ival P enalization C ontrolled C ompetitive L earning for Clustering with Automatic Cluster Number Selection. Advisor : Dr. Hsu Presenter : Ai-Chen Liao Authors : Yiu-ming Cheung. 2005 . TKDE . Page(s) : 1583 - 1588. Outline. Motivation Objective Method RPCL - PowerPoint PPT PresentationTRANSCRIPT
1Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
On Rival Penalization Controlled Competitive Learning for Clustering with
Automatic Cluster Number Selection
Advisor : Dr. Hsu
Presenter : Ai-Chen Liao
Authors : Yiu-ming Cheung
2005 . TKDE . Page(s) : 1583 - 1588
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Method
RPCL RPCCL
Experimental Results Conclusion Personal Opinions
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Motivation
K-means algorithm has at least two major drawbacks:─ It suffers from the dead-unit problem.─ If the number of clusters is misspecified, i.e., k is not equal
to the true cluster number k*, the performance of k-means algorithm deteriorates rapidly.
The performance of RPCL is sensitive to the value of the delearning rate.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
We will concentrate on studying the RPCL algorithm and propose a novel technique to circumvent the selection of the delearning rate.
We further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method ─ RPCL
Advantage :
RPCL can automatically select the correct cluster number by gradually driving redundant seed points far away from the input dense regions.
Drawback :
RPCL is sensitive to the delearning rate. Idea :
ex. In a election campaign…..(more intense)…..
candidates : A 40%
B 35%
C 5%
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method ─ RPCL
cluster centereach input
Winner (move closer)
Rival (move away)
unchanged
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method ─ RPCL
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method ─ RPCCL
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method ─ RPCCL
This penalization control mechanism by
with
compare
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experimental ResultsRPCL : learning rate αC at 0.001, and αr at 0.0001the number of seed points : 30
audience image : 128*128 pixels
epoch :50
original Audience Image RPCL RPCCL
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
RPCCL has novelly circumvented the difficult selection of the deleaning rate.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Personal Opinions
Advantage RPCCL can automatically select the correct cluster n
umber. The novel technique can circumvent the selection of t
he delearning rate.
Drawback limitation : k >= k*
Application clustering…
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.K-means example
1. Given : {2,4,10,12,3,20,30,11,25} k=2
2. Randomly assign means : m1=3 ; m2=4
k1={2,3} , k2={4,10,12,20,30,11,25} ,m1=2.5 , m2=16
k1={2,3,4} , k2={10,12,20,30,11,25} , m1=3 , m2=18
k1={2,3,4,10} , k2={12,20,30,11,25} , m1=4.75 , m2=19.6
k1={2,3,4,10,11,12} , k2={20,30,25} , m1=7 , m2=25
…..
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Dead-unit problem
1. Given : {2,4,10,12,3,20,30,11,25} , k=3
2. Randomly assign means : m1=30 ; m2=25 ; m3=10
Dead-unit
Heuristic Frequency Sensitive Competitive Learning (FSCL) algorithm