Generation and evaluation of distributed cases by clustering of diverse anthropometric data Online publication date: Mon, 10-Oct-2016
by Erik Brolin; Dan Högberg; Lars Hanson; Roland Örtengren
International Journal of Human Factors Modelling and Simulation (IJHFMS), Vol. 5, No. 3, 2016
Abstract: This paper describes a study where diversity in body size, strength and joint range of motion, together with diversity in other capability measurements, is included in the process of generating data for a group of test cases using cluster analysis. Descriptive statistics and correlation data was acquired for 15 variables for different age groups and both sexes. Based on this data, a population of 10,000 individuals was synthesised using correlated random numbers. The synthesised data was used in cluster analyses where three different clustering algorithms were applied and evaluated; hierarchical clustering, k-means clustering and Gaussian mixture distribution clustering. Results from the study show that the three clustering algorithms produce groups of test cases with different characteristics, where the hierarchical and k-means algorithm give the most diverse results and where the Gaussian mixture distribution gives results that are in between the first two.
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