文章基本信息

标题：Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach
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作者：Ayad Hendalianpour ; Jafar Razmi ; Mohsen Gheitasi 等
期刊名称：Accounting
印刷版ISSN：2369-7393
电子版ISSN：2369-7407
出版年度：2017
卷号：3
期号：2
页码：81-94
DOI：10.5267/j.ac.2016.8.003
出版社：Growing Science
摘要：Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC) algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.
关键词：K-mean; C-mean; Fuzzy C-mean; Kernel K-mean; Fuzzy variables; Fuzzy relation clustering (FRC)