Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Abstract

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.Growing Science Ltd. All rights reserved. 7

Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL

“…Some basic methods will be shown to accomplish the purpose of this study. This section describes the fuzzy set theory’s features, the computational model utilized for the FRC algorithm (Hendalianpour et al 2017 ), and FDEMATEL.…”

“…(Hendalianpour et al 2017 ) (Fuzzy relation) Let be two universes of discourse, and a fuzzy subset of is called a fuzzy relationship from to : where is the membership function, reflects the degree of relationship between and …”

“…(Hendalianpour et al 2017 ) (Max–min composition) Considering and as two fuzzy relations of and , max–min composition is defined as follows: …”

“…(Hendalianpour et al 2017 ) (Fuzzy equivalence relations) The fuzzy relation on represents a fuzzy equivalence relation by meeting the following three conditions: 1. Reflexive .…”

“…In reality, however, data is often multiple and inconsistent, it is not just a single binary or fuzzy. Therefore, Hendalianpour et al ( 2017 ) proposed a novel clustering algorithm called fuzzy relation clustering (FRC) that can simultaneous processing both crisp, fuzzy quantity and linguistic variables. Table 1 shows the comparisons of the popular clustering algorithms.…”