Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6868229 | Robotics and Computer-Integrated Manufacturing | 2015 | 8 Pages |
Abstract
Data mining (DM) techniques have been used to solve marketing and manufacturing problems in the fashion industry. These approaches are expected to be particularly important for highly customized industries because the diversity of products sold makes it harder to find clear patterns of customer preferences. The goal of this project was to investigate two different data mining approaches for customer segmentation: clustering and subgroup discovery. The models obtained produced six market segments and 49 rules that allowed a better understanding of customer preferences in a highly customized fashion manufacturer/e-tailor. The scope and limitations of these clustering DM techniques will lead to further methodological refinements.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Pedro Quelhas Brito, Carlos Soares, SĂ©rgio Almeida, Ana Monte, Michel Byvoet,