Sentence examples for mean cluster analysis from inspiring English sources

UPGMA (un-weighted pair group method with arithmetic mean) cluster analysis was conducted usizSpc 2.2 software.

UPGMA (unweighted pair group method with arithmetic mean) cluster analysis was conducted using software NTSYSpc 2.2 [ 41].

Unweighted pair group method with arithmetic mean cluster analysis (UPGMA) was performed in PopGene 1.32 using Nei's genetic distance (Nei 1972) to analyse the patterns of population-level genetic distances across all populations for both the AFLP and SSR data sets.

First, BioNumerics software version 6.6 (Applied Maths, Sint-Martens-Latem, Belgium) was used to compare PFGE profiles of human and bovine fecal isolates by conducting an UPGMA (unweighted pair group method with arithmetic mean) cluster analysis using the Dice similarity coefficient, with a band matching tolerance of 1%.

PFGE profiles of the clinical E. coli O157 H7 isolates were analysed and compared using BioNumerics software (version 6.6) [ 41] to create a dendrogram applying UPGMA (unweighted pair group method with arithmetic mean) cluster analysis using the Dice similarity coefficient, with a band matching tolerance of 1%.

The gel was run for 24 h at 5.6��V cm-1 with pulsed-time ramping 2 5 s at 14 ° C. The analysis of the fragment pattern was performed in BioNumerics® version 7.1 (Applied Maths, Gent, Belgium) on fragments between 9 and 117 kb using the Dice coefficient and Unweighted Pair Group Method with Arithmetic Mean cluster analysis, with position optimization set at 0.5 % and tolerance at 1.2%%.

K-mean cluster analysis was further applied to visualize the underlying morphological basis of spinal cord tissue by chemical component types and their distribution pattern.

The mean NDVI was used as input in a k-mean cluster analysis [89] with the number of clusters set to the number of LUCa (forest land, grassland, cropland and settlements and other land use) used in the analysis.

In the first one we use kernel density function analysis to uncover potential clusters in the Spanish regional relative unemployment rates.19 In a second step, we perform a (k -mean cluster analysis based on exogenous variables to rank -meanregion in one of the groups.

K-means Cluster Analysis (KMCA).

Fuzzy C Means Cluster Analysis (FCMCA).