Customer Relationship Management (CRM) plays a major role in analyzing customer behavior and the opinions of an organization or enterprise. Data mining methods are widely uutilize to analyze customer data to increase business and revenue. Data mining refers to the extraction of essential and useful information from customer feedback and activities on websites through mining technologies. However, extracting essential information from customer behavior is quite challenging as it requires a detailed analysis of customer desires, requirements, buying patterns, etc., all the information in the e-commerce market is essential for an enterprise as it will support knowing the customer behavior. Deep learning algorithms based on customer behavior classification models are evolved in recent times. However, the performance can be improved if the network parameters are optimized through optimization algorithms. Based on this, a deep stacked autoencoder-based customer behavior classification model is presented in this research work along with the dragonfly optimization algorithm. The network parameters of the deep-stacked autoencoder are optimized using the dragonfly optimization algorithm to attain enhanced classification accuracy. Benchmark customer behavior dataset is used for experimentation and analyzed the performance in terms of recall, precision, f1-score, and accuracy. The proposed optimized deep learning model attains better performance compared to deep learning approaches like Long-Short Term Memory (LSTM), Convolutional Neural network (CNN), and Autoencoder models.
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The authors confirm contribution to the paper as follows:
Conceptualization: Gajendran K S and Arunkumar Thangavelu;
Methodology: Gajendran K S;
Software: Arunkumar Thangavelu;
Data Curation: Gajendran K S and Arunkumar Thangavelu;
Writing- Original Draft Preparation: Gajendran K S;
Visualization: Gajendran K S and Arunkumar Thangavelu;
Investigation: Arunkumar Thangavelu;
Supervision: Arunkumar Thangavelu;
Validation: Gajendran K S;
Writing- Reviewing and Editing: Gajendran K S and Arunkumar Thangavelu; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr.Arunkumar Thangavelu for this research completion and support.
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Arunkumar Thangavelu
School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
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Cite this article
Gajendran K S and Arunkumar Thangavelu, “Customer Behavior Classification Using Deep Stacked Autoencoder with Dragonfly Optimization”, Journal of Machine and Computing, vol.5, no.4, pp. 2230-2240, October 2025, doi: 10.53759/7669/jmc202505173.
