Market segmentation using supervised and unsupervised learning techniques for E-commerce applications

Issue title: Special Section: Intelligent tools and techniques for signals, machines and automation

Guest editors: Smriti Srivastava, Hasmat Malik and Rajneesh Sharma

Article type: Research Article

Authors: Tiwari, Raman^{a; *} | Saxena, Manav Kumar^b | Mehendiratta, Prajna^b | Vatsa, Kshitij^b | Srivastava, Smriti^b | Gera, Rajat^a

Affiliations: [a] Manav Rachna University, Faridabad, India | [b] Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi, India

Correspondence: [*] Corresponding author. Raman Tiwari, Manav Rachna University, Faridabad, 121004, India. E-mail: [email protected].

Abstract: Market Segmentation has been a key area of implementation of soft computing techniques in E-commerce applications. Various techniques have been used to achieve maximum results in the classification of the ecommerce market. From stochastic techniques to neural networks, there is a plethora of techniques that have been applied. In this paper, we use self organising Maps (SOMs) an unsupervised learning technique to study the various factors which can be used to segment the market. On the other hand supervised learning techniques such as Nearest Neighbour (NN) and Support vector machine (SVM) are used to quantitatively classify the purchase behaviour based on various factors. The better classification technique is identified through appropriate measures. Further, evolutionary algorithms are used to augment the performance of these classification techniques. Analysis of the results and various factors affecting it is also performed.

Keywords: Market segmentation, SVM, KNN, PSO, GSA

DOI: 10.3233/JIFS-169818

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5353-5363, 2018