Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

2014 ◽  
Vol 57 (11) ◽  
pp. 1-8 ◽  
Author(s):  
KaiLe Zhou ◽  
Chao Fu ◽  
ShanLin Yang
Keyword(s):  
Parameter Selection ◽  
Cluster Validation ◽  
Fuzzy C Means

scholarly journals COMPARATIVE STUDY OF FUZZY C-MEANS AND K-MEANS ALGORITHM FOR GROUPING CUSTOMER POTENTIAL IN BRAND LIMBACK

2021 ◽  
Vol 3 (4) ◽  
pp. 327-334
Author(s):  
Difa Lazuardi Aditya ◽  
Devi Fitrianah
Keyword(s):  
Optimal Number ◽  
Customer Segmentation ◽  
Cluster Validation ◽  
Fuzzy C Means ◽  
Monetary Analysis ◽  
A Value ◽  
Silhouette Coefficient ◽  
Potential Customers ◽  
Cluster 2 ◽  
Optimal Number Of Clusters

The customer is a stakeholder for a business, to maintain and increase customer enthusiasm and develop it for the company's performance, it is necessary to do customer segmentation which aims to find out potential customers. This study uses purchase transaction data from Brand Limback customers in the period 2020. The use of RFM (Recency, Frecuency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The datasets generated from RFM are grouped using the Fuzzy C-Means and K-Means algorithms, the two algorithms will compare the quality in the formation of clusters using the Silhoutte Coefficient and Davies-Bouldin Index methods. Customer segmentation from the RFM dataset that has been clustered produces 7 optimal clusters, namely Cluster 0 is a bronze customer. Cluster 1 is a silver customer. Cluster 2 is a gold customer. Cluster 3 is a platinum customer. Cluster 4 is a diamond customer. Cluster 5 is a super customer, and cluster 6 is a superstar customer. The cluster validation of k-means using the silhouette coefficient produces a value of 0.934 while the Davies bouldin index produces a value of 0.155 and the validation results of the fuzzy c-means algorithm using the silhouette coefficient produces a value of 0.921 while the Davies bouldin index produces a value of 0.145.

scholarly journals Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation

2018 ◽  
Author(s):  
Md. Abu Bakr Siddique ◽  
Rezoana Bente Arif ◽  
Mohammad Mahmudur Rahman Khan ◽  
Zahidun Ashrafi
Keyword(s):  
Clustering Algorithms ◽  
Two Dimensional ◽  
Cluster Validation ◽  
Fuzzy C Means ◽  
Noise Point ◽  
Tendency Analysis ◽  
Robust To Noise

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Modification of Fuzzy c-Means Method Using a Nonlinear Vector Criterion

2010 ◽  
Vol 42 (12) ◽  
pp. 13-21
Author(s):  
Anatoliy F. Bulat ◽  
Elena M. Kiseleva ◽  
Sergey A. Pichugov ◽  
Oleg B. Blyuss
Keyword(s):  
Vector Criterion ◽  
Fuzzy C Means ◽  
Nonlinear Vector
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