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Other approaches involve the use of clustering algorithms to classify patients into categories based on multiple clinical attributes.
The use of clustering algorithms to group similar conformations is the most appropriate data mining technique to distill the structural information from properties of an MD trajectory [7 10].
These genotypes are usually determined through use of clustering algorithms applied to underlying quantitative measurements (e.g., see [22]).
We describe an approach for identifying outlying samples that does not depend on the choice or use of clustering algorithms.
These concepts are independent of the choice or use of clustering algorithms and depend only on the adjacency measure used to construct the network.
The numerical analyses performed here with the dataset emphasized the use of clustering algorithms for finding hierarchical groups of objects in an unsupervised way [ 72– 72].
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The tool provides access to a widely used set of clustering algorithms and simultaneously allows the interactive visualization of the data.
Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms.
The GSC can be implemented using a variety of clustering algorithms or assignment tests.
The cluster scores are then normalized to a maximum of 1 by dividing by the number of clustering algorithms used.
Hierarchical clustering is another group of clustering algorithms widely used for biological data analysis.
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