Employing data mining to identify the significant rules for classifying body types

Authors:
Chih-Hung Hsu;Su-Chin Chen;Bor-Shong Liu
Affiliations:
Department of Industrial Engineering and Management, Hsiuping Institute of Technology, Taiwan, R.O.C.;Department of Industrial Engineering and Management, St. John's University, Taiwan, R.O.C.;Department of Fashion Imaging, Ming-Dad University, Taiwan, R.O.C.
Venue:
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Year:
2007

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Abstract

The goal of this study was to identify the significant rules of human body types from the anthropometric data of adult males, using novel two stage-based data mining procedure. The development procedure included two phases. First, cluster analysis was conducted to sort cases into clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Second, the decision tree uses rules created in accordance with input variables and is executed with data classification by a tree type demonstration to extract the most significant factors and the significant rules based on the results of cluster analysis. Certain advantages may be observed when the significant rules are identified, using two stage-based data mining procedure. Body types could be accurately classified for physiology, medical treatment, sports talent and garment manufacturing according the newly classification rules. The results of this study can provide an effective procedure of identifying the significant rules for classifying human body type to satisfy the demands for industrial and commerce.