Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey

The vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, [Formula: see text]-means and fuzzy [Formula: see text]-means, based on transaction data that belong to Turkey’s two major cities. Over 10,000 records of customers’ data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.

An Improved Fuzzy C-Means Clustering for Brain MR Images Segmentation

In this paper, an improved fast FCM (HF-KFCM) algorithm was proposed based on histogram statistics of brain MR images. The algorithm firstly uses the multi-scale window traversal method to find the peak point of the histogram, then uses it as the initialization center of fuzzy clustering, and finally uses the fast clustering method based on statistical information to traverse, so as to reduce the computation amount of each iteration. Experimental results show that compared with the standard FCM algorithm and other improved algorithms, the proposed algorithm is significantly improved in clustering effectiveness and fuzzy segmentation.