Title
Market segmentation using supervised and unsupervised learning techniques for E-commerce applications.
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.
Year
DOI
Venue
2018
10.3233/JIFS-169818
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Market segmentation,SVM,KNN,PSO,GSA
Market segmentation,Unsupervised learning,Artificial intelligence,Machine learning,Mathematics,E-commerce
Journal
Volume
Issue
ISSN
35
SP5
1064-1246
Citations 
PageRank 
References 
0
0.34
22
Authors
6
Name
Order
Citations
PageRank
Raman Tiwari100.34
Manav Kumar Saxena200.34
Prajna Mehendiratta300.34
Kshitij Vatsa400.34
Smriti Srivastava513719.60
Rajat Gera670.94