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We produced solutions for predefined cluster numbers of 2 and 3.
We first view each (t_i^c) as a data point on a one-dimensional space and assume that we have a predefined cluster number, K, which can be determined by cross-validations.
Partitioning clustering algorithms, such as K-means, K-medoids PAM, CLARA and CLARANS assign objects into k (predefined cluster number) clusters, and iteratively reallocate objects to improve the quality of clustering results.
In order to detect substructure, we again applied the same Bayesian-based analysis within each predefined cluster.
Unlike some existing genotype-calling algorithms, optiCall estimates the positions of the genotype classes using the given intensity data and does not require a training dataset or predefined cluster file.
Relative frequency was determined by first assessing the absolute number of values found within predefined clusters, followed by expressing the number of values or incidences per predefined cluster as a percentage of the absolute number of all values of a certain parameter, for instance arterial blood glucose.
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The VSSC algorithm generates a new cluster by splitting an existing cluster into two according to a predefined cluster-variance criterion.
In this process a chemist would inspect individual molecular structures and assign them to clusters based on a predefined clustering objective.
This normalised centroid-based stability measure can be used to validate the results of clustering runs with random starting positions, with a variable number of predefined clusters, with a varying data base and even with variable features.
However, the crucial difference is that individuals are recruited from predefined clusters rather than the general patient population.
The program seeks genetic structure from individual multilocus genotypes sampled at different geographical locations without assuming predefined clusters.
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