Measuring to fit: virtual tailoring through cluster analysis and classification

Authors:
Herna L. Viktor;Eric Paquet;Hongyu Guo
Affiliations:
School of IT and Engineering, University of Ottawa, Ottawa, Canada;Visual Information Technology Group, National Research Council of Canada, Ottawa, Canada;School of IT and Engineering, University of Ottawa, Ottawa, Canada
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

Citing 8
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Relational Data Mining

Relational Data Mining
Data Mining: Introductory and Advanced Topics

Data Mining: Introductory and Advanced Topics
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimal postures and positioning for human body scanning

NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Mining relational databases with multi-view learning

MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining

Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures

DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery

Quantified Score

Hi-index	0.01

Visualization

Abstract

Clothes should be designed to tailor well, fit the body elegantly and hide obvious body flaws. To attain this goal, it is crucial to know the interrelationships between different body measurements, such as the interplay between e.g. shoulder width, neck circumference and waist. This paper discusses a study to better understand the typical consumer, from a virtual tailor's perspective. Cluster analysis was used to group the population into five clothing sizes. Next, multi-relational classification was applied to analyze the interplay between each group's anthropometric body measurements. Throughout this study, three- dimensional (3-D) body scans were used to verify the validity of our findings. Our results indicate that different sets of body measurements are used to characterize each clothing size. This information, together with the demographic profiles of the typical consumer, provides us with new insight into our evolving population.