A robust adaptive clustering analysis method for automatic identification of clusters

Presenter : Bo-Sheng Wang 　Authors : P.Y. Mok*, H.Q. Huang, Y.L. Kwok, J.S. Au

PR, 2012

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Outlines

• Motivation• Objectives• Methodology• Experiments• Compary• Conclusions• Comments

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Motivation

• Correct cluster numbers do not guarantee that a data set can be properly partitioned in the desired way.

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Objectives

• The objective of this paper is to propose a robust and adaptive clustering analysis method.

• 1.Produces reliable clustering results

• 2.Identifies the desired cluster number.

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Methodology-Fuzzy C-mean(FCM)

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Methodology-Fuzzy C-mean(Example)

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Methodology-Fuzzy C-mean(Example)

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Methodology-Fuzzy C-mean(Example)

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Methodology-Fuzzy C-mean(Example)

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Mothodology-RAC-FCM

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Mothodology-RAC-FCM

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Mothodology-RAC-FCM

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Mothodology-RAC-FCM

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Mothodology-Adaptive implementation

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Experiments-K-mean

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KM

Experiments-K-mean+RAC-FCM

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Mothodology-Application

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Experiments

• When the distribution of cluster number is not stable enough to give the desired number.

Increasing the upper bound of cluster number can.

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Experiments

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Experiments

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How to verification the proposed k parameter?

Experiments

• This paper use the three widely data sets including the Iris data set, Breast Cancer Wisconsin (Diagnostic) data set and Wine data set.

• Step:1.Verified the distribution stability of the cluster number

2.Compared to different cluster validity index methods.

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Experiments -Iris Data Set

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Experiments - Breast Cancer Wisconsin (Diagnostic) data set

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Experiments - Wine data set

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Experiments -Compary different Data Set

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Compary-Comparison with the spectral clustering method

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RAC-FCM Spectral Clustering Method

WIN

Compary-Comparison with cluster ensembles

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Conclusions

• This paper proposes method no cluster number is needed to define.

• The method is not only robust but also adaptive.

• The method not only identifies the desired cluster number but also ensures reliable clustering results.

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Comments

• Advantages–We can obtain optimum Result use this method in

cluster analysis.

• Disadvantage– This method is very take the time because of a

program.

• Applications– Cluster Analysis

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