Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures

Authors:
Isis Peña;Herna L. Viktor;Eric Paquet
Affiliations:
School of IT and Engineering, University of Ottawa, Ottawa, Canada;School of IT and Engineering, University of Ottawa, Ottawa, Canada;School of IT and Engineering, University of Ottawa, Ottawa, Canada and National Research Council of Canada, Ottawa, Canada
Venue:
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Year:
2009

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Abstract

Clothes should fit consumers well, be aesthetically pleasing and comfortable. However, repeated studies of customers' levels of satisfaction indicate that this is often not the case. For example, more robust males often find it difficult to find pants that are the correct length and fit their waists well. What, then, are the typical body profiles of the population? Would it be possible to identify the measurements that are of importance for different sizes and genders? Furthermore, assuming that we have access to an anthropometric database would there be a way to guide the data mining process to discover only those relevant body measurements that are of the most interest for apparel designers? This paper describes our results when addressing these questions through cluster analysis and interestingness measures-based feature selection. We explore a database containing anthropometric measurements as well as 3-D body scans, of a representative sample of the Dutch population.