A Fuzzy Clustering Approach for Segmenting Retail Industry

Author(s):  
M. Hemalatha

The foremost theme of this chapter is to utilize the subtractive clustering concept for defining the market boundaries in the fuzzy-based segmentation. In this sense, the present work starts by analyzing the importance of segmenting the shoppers on the basis of store image. After reviewing the segmentation literature, the authors performed a segmentation analysis of retail shoppers in India. Researchers often use clustering analysis as a tool in market segmentation studies, the results of which often end with a crisp partitioning form, where one member cannot belong to two or more groups. This indicates that different segments overlap with one another. This chapter integrates the concept of application of subtractive clustering in fuzzy c means clustering for profiling the customers who perceive the retail store based on its image. Fuzzy clustering is also compared with hard clustering solutions. Then the authors predict the model using discriminate analysis. Further, the chapter concentrates on the answer tree model of segmentation to identify the best predictor. Main conclusions with implications for retailing management are shown.

Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.


1970 ◽  
Vol 24 (6) ◽  
pp. 455-467 ◽  
Author(s):  
Iman Aghayan ◽  
Nima Noii ◽  
Mehmet Metin Kunt

This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error) and response time (t). The highest R-value was obtained for the multi-layer perceptron (0.89), demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second), 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.


2008 ◽  
Author(s):  
Britta Cornelius ◽  
Martin Natter ◽  
Corinne Faure
Keyword(s):  

Author(s):  
Weiping Ding ◽  
Shouvik Chakraborty ◽  
Kalyani Mali ◽  
Sankhadeep Chatterjee ◽  
Janmenjoy Nayak ◽  
...  

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