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Authors: Isis Peña 1 ; Herna Lydia Viktor 1 and Eric Paquet 2

Affiliations: 1 University of Ottawa, Canada ; 2 National Research Council of Canada, Canada

ISBN: 978-989-674-011-5

Keyword(s): Cluster analysis, Classification, Anthropometry, Interestingness measures-based data mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Business Intelligence Applications ; Clustering and Classification Methods ; Data Analytics ; Data Engineering ; Data Reduction and Quality Assessment ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems

Abstract: In the apparel industry, an important challenge is to produce garments that fit various populations well. However, repeated studies of customers’ levels of satisfaction indicate that this is often not the case. The following questions come to mind. What, then, are the typical body profiles of a population? Are there significant differences between populations, and if so, which body measurements need special care when e.g. designing garments for Italian females? Within a population, would it be possible to identify the measurements that are of importance for different sizes and genders? Furthermore, assume that we have access to an accurate anthropometric database. Would there be a way to guide the data mining process to discover only those body measurements that are of the most interest for apparel designers? This paper describes our results when addressing these questions. To this end, we explore a database, containing anthropometric measurements and 3-D body scans, of samples of the North American, Italian and Dutch populations. Our results show that we accurately discover the relevant subsets of body measurements, through the use of objective interestingness measures-based feature selection and feature extraction, for the various body sizes within each population and gender. (More)

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Paper citation in several formats:
Peña I.; Viktor H.; Paquet E. and (2009). EXPLORATIVE DATA MINING FOR THE SIZING OF POPULATION GROUPS.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 152-159. DOI: 10.5220/0002292601520159

@conference{kdir09,
author={Isis Peña and Herna Lydia Viktor and Eric Paquet},
title={EXPLORATIVE DATA MINING FOR THE SIZING OF POPULATION GROUPS},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={152-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002292601520159},
isbn={978-989-674-011-5},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - EXPLORATIVE DATA MINING FOR THE SIZING OF POPULATION GROUPS
SN - 978-989-674-011-5
AU - Peña, I.
AU - Viktor, H.
AU - Paquet, E.
PY - 2009
SP - 152
EP - 159
DO - 10.5220/0002292601520159

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