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

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. | Comparing clustering models in bank customers Based on Fuzzy relational clustering approach Accounting 3 2017 81 94 Contents lists available at GrowingScience Accounting homepage ac Comparing clustering models in bank customers Based on Fuzzy relational clustering approach Ayad Hendalianpoura Jafar Razmia and Mohsen Gheitasib a School of Industrial Engineering College of Engineering Tehran University Tehran Iran b School of Industrial Engineering College of Engineering Shiraz Azad University Shiraz Iran CHRONICLE ABSTRACT Article history Clustering is absolutely useful information to explore data structures and has been employed Received December 5 2015 in many places. It organizes a set of objects into similar groups called clusters and the objects Received in revised format within one cluster are both highly similar and dissimilar with the objects in other clusters. The February 16 2016 K-mean C-mean Fuzzy C-mean and Kernel K-mean algorithms are the most popular Accepted August 15 2016 Available online clustering algorithms for their easy implementation and fast work but in some cases we cannot August 16 2016 use these algorithms. Regarding this in this paper a hybrid model for customer clustering is Keywords presented that is applicable in five banks of Fars Province Shiraz Iran. In this way the fuzzy K-mean relation among customers is defined by using their features described in linguistic and C-mean quantitative variables. As follows the customers of banks are grouped according to K-mean Fuzzy C-mean C-mean Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Kernel K-mean Clustering FRC algorithm. The aim of this paper is to show how to choose the best clustering Fuzzy variables algorithms based on density-based clustering and present a new clustering algorithm for both Fuzzy relation clustering FRC crisp and fuzzy variables. Finally we apply the proposed approach to five datasets of customer s .