Abstract
Nowadays, most companies are utilizing customer behavior mining frameworks to improve their business strategies. These frameworks are used to predict different business patterns, such as sales, forecasting, or marketing. Different data mining and machine learning concepts have been applied to predict customer behaviors. However, traditional approaches consume more time and fail to predict exact user behaviors. In this paper, intelligent techniques, such as fuzzy clustering and deep learning approaches, are utilized to investigate customer portfolios to detect customers' purchasing patterns. To accomplish this objective, hier-archical fuzzy clustering was applied to compute the relationship between products and purchasing criteria. According to the analysis, similar data are grouped together, which reduces the maximum error classifica-tion problem. Then, an optimized deep recurrent neural network is incorporated into this process to improve the prediction rate. The discussed system efficiency is evaluated using a number of datasets with respec-tive performance metrics. The proposed approach was compared to other single model-based and hybrid model-based approaches and was found to attain maximum accuracy and minimum error rate in comparison.