Exploring Anthropometric Data Through Cluster Analysis

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ConferenceDigital Human Modeling for Design and Engineering (DHM), June 15-17, 2004., Oakland University, Rochester, Michigan, USA
AbstractAnthropometric databases consisting of both multimedia and relational content are increasingly becoming commonplace. These databases are huge and contain data with diverse formats, representations and models. Data mining provides a powerful mechanism to further explore and explain the data as contained in these heterogeneous repositories, focusing on discovering new relationships which cannot be found using standard information retrieval techniques. In particular, cluster analysis is a data mining technique which is used to group data records into unlabeled classes, e.g. to group individuals with similar body types, income and education levels into a cluster, using unsupervised learning.<br /><br />This paper introduces cluster analysis as a method to explore 3D body scans together with the relational anthropometric and demographic data as contained in an integrated multimedia anthropometric database. The paper provides an overview of different cluster analysis algorithms and discusses the strengths and weaknesses of each approach when mining 3D objects together with relational attributes. Cluster analysis algorithms are evaluated in terms of scalability, the number of attributes that can be processed, the level of human intervention required and the characteristics of the clusters, amongst others. This is followed by a discussion on the application of cluster analysis to anthropometric data. The use of cluster analysis to group the data records into clusters based on both the 3D body scans and the relational attributes lead to a new understanding of the data and their interrelationships.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number46564
NPARC number5765179
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Record identifier43d0d9ac-8646-4fb4-a08f-6f890fb657c5
Record created2009-03-29
Record modified2016-05-09
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