weighted cluster ensembles: methods and analysis

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

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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

weighted cluster ensembles: methods and analysis

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