Abstract
Article history: Received December 5, 2015 Received in revised format February 16 2016 Accepted August 15 2016 Available online August 16 2016 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 © 201
Highlights
Clustering has been a widely studied problem in the machine learning literature (Filippone et al, 2008; Jain, 2010)
We compare the output of popular clustering algorithms (K-mean, C-mean, Fuzzy C-mean and Kernel K-mean) and fuzzy relation clustering algorithm based on four dataset of customers segmentation in banks of Fars Province, Shiraz, Iran
In order to prove the clustering algorithms, five data sets are run with Kmean, C-mean, Fuzzy C-mean, Kernel K-mean and Fuzzy Relation Clustering (FRC) algorithm, and the results are evaluated and compared respectively in terms of the objective function of density-based evaluation algorithm
Summary
Clustering has been a widely studied problem in the machine learning literature (Filippone et al, 2008; Jain, 2010). There is an enormous variety of agglomerative algorithms in the literature: single-link, complete-link, and average-link (Höppner, 1999; Akman, 2015). The average-link algorithm builds a compromise between the two extreme cases of single-linkage and complete-linkage (Eberle et al, 2012; Lee et al, 2005; Clir, & Yuan, 1995). The popular clustering algorithms has been widely used to solve problems in many areas, for instance the K-mean is very sensitive to initialization, the better centers we choose, the better results we get (Khan & Ahmad, 2004; Núñez et al, 2014), but has some of weakness and we can't use this algorithm everywhere and this algorithm can't get crisp, fuzzy and linguistic variables together.
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