According to the strain hardening exponent n in the Ramberg-Osgood material model, cluster analysis is conducted to separate these strength curves into two groups.
Using the technique of Normal-Mixture-Model Cluster Analysis [14] where six models/scenario were used resulted in the best model which included the simultaneous use of the three variables DPI3-weighted, No. of Total Hazards and No. of Low-speed Warnings.
All images were partitioned into grey and white matter (GM/WM), cerebrospinal fluid (CSF) and background using a modified mixture model cluster analysis, after correcting for non-uniformities in image intensity [20].
To correct for non-uniformity in image intensity, the SPM segmentation employs a mixture model cluster analysis to identify voxel intensities, which matches particular tissue types, in combination with a priori probabilistic knowledge of the spatial distribution of tissues derived from the GM, WM, and CSF of the prior probability images (i.e., the priors) [29], [30].
For example, by incorporating a prior noise-distribution to improve the parameter estimation in the commonly used CAGED model (Cluster Analysis of Gene Expression Dynamic, Wangg et al. achieved more functional and meaningful clusters, as validated by Gene Ontology [ 43].
Finally, using a modified mixture model cluster analysis, normalized images were corrected for non-uniformities in signal intensity and partitioned into grey and white matter, CSF and background [ 6].
Mixed model clustering analysis identified three risk groups with probabilities for low-risk <0.333; intermediate-risk 0.333-0.606; and high-risk ≥0.606.
Combining a spatially explicit and dynamic ecological-economic simulation model with cluster analysis we investigate how the coexistence of both species depends on the design of the tradable permit scheme, and how the effective scheme design (i.e. the scheme design that maximises coexistence) depends on the biological characteristics of the two species.
Other researchers [13, 14, 15], analyze data traffic on a daily basis (hours and days of week), trying to develop traffic flow models using cluster analysis.
Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified.
