CN2-SD for subgroup discovery in a highly customized textile industry: A case study

• Type: Chapter or Part of a Book
• Date: 2013

Authors

Almeida, S (Author) Other; Carlos Soares (Author) FEUP

Indexing

Scopus - 0 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00K-373

DOI: 10.1007/978-3-319-00557-7_48

Abstract (EN): The success of the textile industry largely depends on the products offered and on the speed of response to variations in demand that are induced by changes in consumer lifestyles. The study of behavioral habits and buying trends can provide models to be integrated into the decision support systems of companies. Data mining techniques can be used to develop models based on data. This approach has been used in the past to develop models to improve sales in the textile industry. However, the discovery of scientific models based on subgroup discovery algorithms, that characterize subgroups of observations with rare distributions, has not been made in this area. The goal of this work is to investigate whether these algorithms can extract knowledge that is useful for a particular kind of textile industry, which produces highly customized garments. We apply the CN2-SD subgroup discovery method to find rare and interesting subgroups products on a database provided by a manufacturer of custom-made shirts. The results show that it is possible to obtain knowledge that is useful to understand customer preferences in highly customized textile industries using subgroup discovery techniques. © Springer International Publishing Switzerland 2013.

Language: English

Type (Professor's evaluation): Scientific