Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
Subject Areas : Data Mining
Motahareh Ghavidel
1
,
meisam Yadollahzadeh tabari
2
*
,
Mehdi Golsorkhtabaramiri
3
1 - Department of Computer Engineering, Islamic Azad University, Babol Branch, Babol, Iran.
2 - Department of Computer Engineering, Islamic Azad University, Babol Branch, Babol, Iran.
3 - Department of Computer Engineering, Islamic Azad University, Babol Branch, Babol, Iran.
Keywords: Customer Clustering, Fuzzy C-Means, Cuckoo Optimization, Convolutional Neural Networks,
Abstract :
To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers.
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