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Essentially, any data clustering method can be used for this purpose.
A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition.
The natural break classification scheme, also called the Jenks classification method, is a data clustering method designed to determine the best arrangement of values into different classes.
Although the SC algorithm is a simple and fast data clustering method with robust properties against outliers and noise; it has two limitations.
FCM is a data clustering method in which a dataset is grouped into N prespecified clusters, with every data point in the dataset belonging to every cluster to a certain degree [11].
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Because there is not any training data, clustering methods iterate between segmenting the image and characterizing the properties of each class.
In this study, the Self-Organizing Maps (SOM) as the intelligent data clustering methods are compared with the statistical clustering approaches including the Hierarchical Cluster Analysis (HCA) and K-means clustering to characterize and identify electrofacies.
Among them, the use of sampling techniques has proved to be especially useful for data clustering methods that perform several iterations considering different initializations, such as CLARANS [19] and PAM [2].
Data clustering methods are commonly used to investigate microarray data.
To identify meaningful classes of genes regulated exclusively by HU, we developed an unsupervised data-clustering method able to avoid numerous single-gene hypotheses by partitioning the transcriptome profiling data into an optimal and biologically relevant number of clusters and by removing the interference of the unwanted growth phase variable.
Thus, these parameters are usually extracted from the observed data using clustering method.
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