This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Exploring Anthropometric Data through Cluster Analysis
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 15, 2004 by SAE International in United States
Annotation ability available
Anthropometric 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.
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.
- Osama Abdali - University of Ottawa, Ottawa (Ontario), Canada
- Herna Viktor - University of Ottawa, Ottawa (Ontario), Canada
- Eric Paquet - Visual Information Technology, National Research Council, Ottawa (Ontario), Canada
- Marc Rioux - Visual Information Technology, National Research Council, Ottawa (Ontario), Canada
CitationAbdali, O., Viktor, H., Paquet, E., and Rioux, M., "Exploring Anthropometric Data through Cluster Analysis," SAE Technical Paper 2004-01-2187, 2004, https://doi.org/10.4271/2004-01-2187.
- Han J. and Kamber M.. Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, USA. 2001.
- Paquet E. and Rioux M.. Anthropometric Visual Data Mining: A Content-Based Approach, submitted to IEA 2003 - International Ergonomics Association XVth Triennial Congress, Seoul, South Korea. August 24–29, 2003.
- Paquet, E. Robinette K. and Rioux. M. Management of Three-dimensional and Anthropometric Databases: Alexandria and Cleopatra. Journal of Electronic Imaging, Volume 9(4). October 2000.
- Dunham M.. Data Mining: Introductory and Advanced Topics. Pearson Education, New Jersey, USA. 2003.
- Berkhin P.. Survey of Clustering Data Mining Techniques. Accrue Software Inc., San Jose, CA, USA. 2002.
- Kolatch E.. Clustering Algorithms for Spatial Databases: A Survey. Department of Computer Science, University of Maryland, USA. 2001.
- Ng R. and Han J.. Efficient and effective clustering methods for spatial data mining. In Proceedings of the VLDB Conference, Santiago, Chile. 1994.
- Kaufman L. and Rousseeuw P.. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley Sons, New York, USA. 1990.
- Sudipto G., Rastogi R. and Shim K.. CURE: An Efficient Clustering Algorithm for Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data. New York, USA. 1998.
- Sheikholeslami G., Chatterjee S. and Zhang A.. WaveCluster: A Multi-resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th Conference on VLDB, 428–439, New York, USA. 1998.
- Agrawal R., Gehrke J., Gunopulos D. and Raghavan P.. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proceedings of the ACM SIGMOD Conference, 94–105, Seattle, WA, USA. 1998.
- Zhao Y. and Karypis G.. Clustering in Life Sciences. In “Functional Genomics: Methods in Molecular Biology”. Humana Press, New Jersey, USA. 2003.
- Ben-Hur A. and Guyon I.. Detecting Stable Clusters Using Principal Component Analysis. In Methods in Molecular Biology, Brownstein M.J. and Kohodursky A. (eds.). Humana Press, New Jersey, USA. 2003.
- Bellman R.. Adaptive Control Processes: A Guided Tour. Princeton University Press. 1961.