An Interval Valued Hesitant Fuzzy Clustering Approach for Location Clustering and Customer Segmentation

2017 ◽  
pp. 56-70 ◽  
Author(s):  
Sultan Ceren Öner ◽  
Başar Öztayşi
Keyword(s):  
Fuzzy Clustering ◽  
Customer Segmentation ◽  
Clustering Approach ◽  
Location Clustering ◽  
Interval Valued

scholarly journals An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 from Radiological Images

2021 ◽  
pp. 1-1
Author(s):  
Weiping Ding ◽  
Shouvik Chakraborty ◽  
Kalyani Mali ◽  
Sankhadeep Chatterjee ◽  
Janmenjoy Nayak ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Early Screening ◽  
Radiological Images ◽  
Clustering Approach ◽  
Unsupervised Fuzzy Clustering

Improved fuzzy clustering approach: Application to medical image MRI

2012 ◽  
Author(s):  
Noureddine El Harchaoui ◽  
Samir Bara ◽  
Mounir Ait Kerroum ◽  
Ahmed Hammouch ◽  
Mohamed Ouadou ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Approach

A Soft Computing Approach to Customer Segmentation

2016 ◽  
pp. 119-146
Author(s):  
Abdulkadir Hiziroglu
Keyword(s):  
Soft Computing ◽  
Secondary Data ◽  
Customer Segmentation ◽  
Two Stage ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Unique Customer ◽  
Managerial Implications ◽  
Clustering Approach ◽  
Retail Company

There are a number of traditional models designed to segment customers, however none of them have the ability to establish non-strict customer segments. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation, they are not based on the effective two-stage methodology. The aim of this study is to propose a two-stage segmentation model based on soft computing using the purchasing behaviours of customers in a data mining framework and to make a comparison of the proposed model with a traditional two-stage segmentation model. Segmentation was performed via neuro-fuzzy two stage-clustering approach for a secondary data set, which included more than 300,000 unique customer records, from a UK retail company. The findings indicated that the model provided stronger insights and has greater managerial implications in comparison with the traditional two-stage method with respect to six segmentation effectiveness indicators.

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