A robust adaptive clustering analysis method for automatic identification of clusters Presenter :...
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![Page 1: 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,](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bff41a28abf838cbd2b5/html5/thumbnails/1.jpg)
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
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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?
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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
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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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