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,

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Presentation transcript:

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,

Outlines Motivation Objectives Methodology Experiments Compary Conclusions Comments 2

Motivation Correct cluster numbers do not guarantee that a data set can be properly partitioned in the desired way. 3

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. 4

Methodology- Fuzzy C-mean(FCM) 5

Methodology- Fuzzy C-mean(Example) 6

7

8

9

Mothodology- RAC-FCM 10

Mothodology- RAC-FCM 11

Mothodology- RAC-FCM 12

Mothodology- RAC-FCM 13

Mothodology- Adaptive implementation 14

Experiments- K-mean 15 KM

Experiments- K-mean+RAC-FCM 16

Mothodology- Application 17

Experiments When the distribution of cluster number is not stable enough to give the desired number. Increasing the upper bound of cluster number can. 18

Experiments 19

Experiments 20

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. 21

Experiments - Iris Data Set 22

Experiments - Breast Cancer Wisconsin (Diagnostic) data set 23

Experiments - Wine data set 24

Experiments - Compary different Data Set 25

Compary- Comparison with the spectral clustering method 26 RAC-FCMSpectral Clustering Method WIN

Compary- Comparison with cluster ensembles 27

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. 28

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 29