Topology-Based Hierarchical Clustering of Self-Organizing Maps

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Intelligent Database Systems Presenter : WU, MIN-CONG Authors : KADIM TA¸SDEMIR, PAVEL MILENOV, AND BROOKE TAPSALL 2011,IEEE Topology-Based Hierarchical Clustering of Self-Organizing Maps

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Topology-Based Hierarchical Clustering of Self-Organizing Maps. Presenter : Wu, Min-Cong Authors : Kadim Ta¸sdemir , Pavel Milenov , and Brooke Tapsall 2011,IEEE. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. - PowerPoint PPT Presentation

Transcript of Topology-Based Hierarchical Clustering of Self-Organizing Maps

Page 1: Topology-Based Hierarchical Clustering of Self-Organizing Maps

Intelligent Database Systems Lab

Presenter : WU, MIN-CONG

Authors : KADIM TA¸SDEMIR,

PAVEL MILENOV, AND BROOKE TAPSALL

2011,IEEE

Topology-Based Hierarchical Clustering ofSelf-Organizing Maps

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Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

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Intelligent Database Systems Lab

Motivation• Hierarchical clustering various distance-based

similarity measures that have some flaw .

• 1. sensitivity to inhomogeneous within-cluster

density distributions, noise or outliers.• 2. depend on the cluster centroids and dispersion around these centroids.

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Intelligent Database Systems Lab

Objectives• we employ average linkage for hierarchical clustering

of prototypes based on CONN so that at each

agglomeration step we merge the pair with maximum

average between cluster connectivity that method

CONN linkage, and add a new similarity criteria

CONN_Index.

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Intelligent Database Systems Lab

Methodology-SOMs • Adapted BMU

• Updating BMU

• RFi and RFij

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Intelligent Database Systems Lab

Methodology-Connectivity Martix

P1 P2 P3

P1 0 3 5

P2 3 0 2

P3 5 2 0

from

CONN(P1,P2) = 3CONN(P1,P3) = 5CONN(P2,P3) = 2CONN(P3,P2) = 2CONN(P3,P1) = 5CONN(P2,P1) = 3

CONNP1 P2 P3

P1 0 3 5

P2 3 0 2

P3 5 2 0

CONN

P1 P2 P3

P1 0 1 3

P2 2 0 1

P3 2 1 0

CADJ

CONN=CADJ(p1,p2)+CADJ(p2,p1) =1+2 =3

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Methodology-CONN Linkage

S1 S2 S3 S4S1 0 1 2 6S2 1 0 3 4S3 2 3 0 5S4 6 4 5 0

Similary matrixS2 S3

S2 0 3

S3 3 0Delete Add

S2 S3 N1

S2 0 3 5

S3 3 0 4

N1 5 4 0

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Intelligent Database Systems Lab

Minimum is better

Maximum is better

Maximum is better

Maximum is better

Maximum is better

nearest 1 is better

Methodology-Number of cluster

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Intelligent Database Systems Lab

Methodology-Applicability and Complexity of the Algorithm

represent the data topology

Delaunay graph is to have dense enough prototypes.

occasionally

CONN Linkage’s time complexity = O(p^2*d)

Average Linkage’s time complexity = O(p^3*d)

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Experiment

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Intelligent Database Systems Lab

Conclusions• CONN linkage produces partitionings better than the

ones obtained by distance-based linkages.

• Conn_Index based on CONN graph provided better decisions than other indices in the study reported in this paper.

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Intelligent Database Systems Lab

Comments• Advantages– CONN_Index provides the best partitioning for the

preset number of clusters.– CONN linkage is mainly proposed for accurate

clustering of remote sensing imagery

• Applications– hierarchical clustering.


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