期刊
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
卷 36, 期 -, 页码 93-100出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2014.12.014
关键词
Customized manufacturing; Fashion industry; Segmentation; Clustering; Subgroup discovery
资金
- European Union's Seventh Framework Programme (FP7) [260169]
- ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness)
- QREN, project PTXXI - Fundo Europeu de Desenvolvimento Regional (FEDER) [2012/24708-NORTE-07-0124-FEDER-000057]
- QREN, project CreativeRetail (Novas tecnologias e paradigmas da computacao em ambientes inteligentes na criacao de um produto inovador para o retalho) - Fundo Europeu de Desenvolvimento Regional (FEDER) [2012/24708-NORTE-07-0124-FEDER-000057, 2012/24708]
- North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF) [NORTE-07-0124-FEDER-000059, NORTE-07-0124-FEDER-000057]
- national funds, through the Portuguese Funding Agency, Fundacao para a Ciencia e a Tecnologia (FCT)
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. (C) 2015 Elsevier Ltd. All rights reserved.
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