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Clustering was performed using the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) hierarchical algorithm with GeneANOVA software.
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Various cluster analysis algorithms like K-means, Hierarchical Cluster Analysis (HCA) etc. are also considered as unsupervised methods.
Currently, methods such as k-means, hierarchical cluster analysis (HCA) and neural networks (to name but a few) are frequently mentioned in the scientific literature concerning drug design.
The analysis uses the k-means, hierarchical and self-organizing map clustering to explore potential clusters or regions; statistical tests are then applied to identify homogeneous regions for subsequent regional frequency analysis.
It provides gene lists and hierarchical matrices using co-expression analysis by distance-base clustering (k-means, hierarchical clustering), as well as integrated gene expression analyses by mapping observed gene expression changes onto specific signaling and metabolic pathways.
We adopted three clustering methods, k-means, hierarchical clustering and spectral clustering [ 61].
That means hierarchical representation of clusters is laminar family of sets.
Chipster's tools for unsupervised clustering include K-means, hierarchical and quality threshold clustering and SOM.
K-means hierarchical clustering was therefore performed on the resulting pair-wise comparisons of all included DNA sequences.
Minimal system requirements are specified at the supplementary material web site, together with instructions and the binaries of K-means, hierarchical methods and NMF are also provided.
A variety of mathematical and computational models have been established for the analysis of network inference that range from data-driven modeling [ 107] using multiple clustering analysis (e.g., K-means, hierarchical, or self-organizing maps).
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