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A Similarity-Based Robust Clustering Method

Author : Miin-ShenYang and Kuo-Lung WuReporter : Tze Ho-Lin

2006/2/8

PAMI, 2004

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Outline

Motivation Objectives Methodology Evaluation Conclusion Personal Comments Appendix

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Motivation

Most clustering methods are less to include the property of robustness.

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Objectives

Construct a robust clustering method that Robust to the initialization (cluster number and

initial guesses) Robust to cluster volumes (ability to detect

different volumes of clusters) Robust to noise and outliers

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Methodology

γ=1

γ=10

5 iteration

converge

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EvaluationFCM PCM

SCM with single-link method

Data set

SCM with Ward’s methodSCM convergence state

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Evaluation

CCA a good estimate of γalways falls in the interval [5,20]

SCA AHC

PCM & FCM

For all n data points in s-dimensional space

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Conclusion

CCA is used to estimate parameter γ. SCA is used to self-organize the data AHC is used to obtain the optimal cluster number c* and identify these c* clusters.

The robustness to different cluster shapes should be another robust clustering characteristic that will be a further research topic.

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Personal Comments

Application Low-dimensional data space clustering

Advantage SCM can achieve robust clustering results

Disadvantage Compared with other clustering method, SCM requires

more computational time.

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Appendix: The Robust properties to noise and outliers

(20)

(21)

φfunction of our estimate

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Correlation Comparison Algorithm (CCA)

γ=5

γ=10

(7)

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Similarity Clustering Algorithm (SCA)

(10)

(11) (5)

5 iteration

converge

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Agglomerative Hierarchical Clustering (AHC)

Fig 4. The Hierarchical Clustering treeFig 5. The identified clusters