weighted cluster ensembles: methods and analysis
DESCRIPTION
Weighted Cluster Ensembles: Methods and analysis. Presenter : Chien-Hsing Chen Author: Carlotta Domeniconi Muna Al- Razgan. 2009.TKDD.40. Outline. Motivation Objective Overall of clustering ensemble Method Experiments Conclusion Comment. Motivation. High-dimensional - PowerPoint PPT PresentationTRANSCRIPT
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
Weighted Cluster Ensembles:Methods and analysis
Presenter: Chien-Hsing Chen
Author:
Carlotta Domeniconi
Muna Al-Razgan
1
2009.TKDD.40..
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Outline Motivation Objective Overall of clustering ensemble Method Experiments Conclusion Comment
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High-dimensional A dimension (feature) is highly relevant to a cluster, but is irrelevant to
another cluster.
Common global dimensionality reduction techniques are unable to capture such local structure of the data.
it instead of using an equal weight for all w1, w2, …, wD.
using an equal weight for a wi among all clusters, where i =1, …, D,
Clustering ensemble An ensemble bag includes: K-means, SOM, … etc Alternative bag is: 3-means, 5-means, 7-means
How can a technique combine the two respects?
Motivation
baseball homerun shopping c1={sport}
c2={auction}
attribute name
w=(0.9, 0.8, 0.1)t
w=(0.1, 0.2, 0.9)t
w1,i ≠ w2,i
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High-dimensional provide a first attempt to capture local structure of the data. LAC-h approach
Clustering ensemble LAC-1, LAC-3, LAC-29, …
Combine the two respects WSPA approach WBPA approach WSBPA approach
Objective
w1,i ≠ w2,i
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Clustering ensemble
Overall work
s ( ) > s ( )
0.95 0.01
0.25
0.20
0.20
0.15
1. A new clustering approach is discussed• handle high-D
2. Three ensemble techniques are introduced• consensus function
3. Graph cut
clusteringpartition
0.13
0.91
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Clustering ensemble
distance of a attribute i within a cluster j
LAC (locally adaptive clustering)
0.9 0.20
0.1 0.22
0.2 0.21
0.7 0.23
c1
c2
w=(0.9, 0.5, 0.1)t
w=(0.1, 0.5, 0.9)t
|nc1| = 4
|nc2| = 3
?
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Clustering ensemble
Overall work
s ( ) > s ( )
0.950.01
0.25
0.20
0.20
0.15
1. A new clustering approach is discussed• handle high-D
2. Three ensemble techniques are introduced• consensus function
3. Graph cut
c1
0.13
0.91
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WSPA 1/2 s ( )
0.94
0.02
0.04
0.060.90 0.04
P =(0.94, 0.04, 0.02)t
P =(0.90, 0.06, 0.02)t
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Clustering ensemble
Two points have high similarity score if often appearing in the same partitions. Instance-based Graph cut
WSPA 2/2
0.950.01
0.25
0.20
0.20
0.15
0.13
0.91
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Problem definition
WBPA 1/3
…
and are never clustered together
the groups to which and belong share the same instances
≡ 0
0.940.02
0.04
0.910.03 0.06 P =(0.94, 0.04, 0.02)t
P =(0.03, 0.91, 0.06)t
Graph
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WBPA 2/3
The Graph is connect between a cluster and an instance instead of that among data
Graph
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WBPA 3/3
0.940.64
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WSBPA
0.94
0.64
0.93
0.94 0.91
0.86 0.85
0.89
0.930.94
0.86
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WSBPA
0.94
0.64
0.01
0.04 0.01
0.89
0.86 0.85
0.89
0.030.04
0.86
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Experiment
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Experiment
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Experiment
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Experiment
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Experiment
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Experiment w1,i ≠ w2,i
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Experiment
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High-dimensional LAC-h approach
Clustering ensemble LAC-1, LAC-3, LAC-29, …
Combine the two respects WSPA approach WBPA approach WSBPA approach
Conclusion
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Comment Advantage
Consensus function
Drawback
Application Ensemble clustering on SOM