To characterize dietary patterns using two different cluster analysis strategies.
The "unique shapes" for each volume were computed using two different clustering strategies, the "small-then-large" approach and the "large-then-small" approach, as depicted in Figure 6, and detailed procedures are described in the Materials and Methods section.
To investigate the shape space redundancy between different volumes, the unique shapes (Figures 4 and 5) for each volume were generated using two different clustering schemes: (1) the "small-then-large" method and (2) the "large-then-small" method (Figure 6).
To guard against false cluster discovery, we employed an objective measure of cluster validity, and reproduced the result using two different clustering algorithms, in two independent datasets.
We performed such analysis using three different clustering methods: K-means, K-means spectral and hierarchical clustering.
Gene expression data were collected and used as a training set to build a classification model, using four different clustering algorithms.
Our analysis used two different clustering tools to subgroup the compiled haplotypes, namely the Bayesian clustering and network analysis.
We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network.
We analyzed the structure using two different methods of clustering, the first based on a Bayesian approach [ 27], and the second using Ward's hierarchical clustering method [ 28, 29].
We systematically compared the treponeme communities present within each of twenty periodontitis and periodontitis-free subjects using two different types of cluster analysis: one that was OTU-based (i.e. taxonomy-based), and another that was phylogeny-based.
