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In [20], the authors proposed altering the procedure of data clustering and feature selection iteratively.
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Regional differences across centers may cause data clustering via sociocultural characteristics of patients or procedures.
The outlined procedure of approximating data using orthogonal basis functions (here polynomials) and clustering estimated coefficients appears quite naturally in the context of functional data analysis [ 9- 14] 14].
The statistical significance of differences was tested by estimates of means (SVYMEAN and SVYLC procedure) for continuous variables and by the chi-squared test for categorical variables (SVYTAB procedure), corrected for clustered data and converted into F-statistics.
Hierarchical clustering of data was performed with Cluster 3.0 [3.0.
The most important benefit of this two-layer procedure is that computational load decreases considerably in comparison with data based clustering methods, making it possible to cluster large data sets and to consider several different clustering alternatives in a limited time.
There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately.
The ClusterSim R package was used to search for the optimal clustering procedure for each domain of data.
Weighted networks give rise to data reduction methods, clustering procedures, visualization methods, data exploratory methods, and intuitive approaches for integrating disparate data sets.
In order to assess the quality and reproducibility of our Me-DIP-chip procedure the hierarchical clustering of the raw data was performed using Partek Genomic Suite (PGS) software before and after the background normalization (Figure S1A).
This paper compares and contrasts the applications of crisp and fuzzy set-theoretic-based clustering procedures to a set of data describing the cognitive and intellectual functioning of a group of subjects participating in a longitudinal study of aging.
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