Determination of K Using Peak Counts of Features for Clustering

The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.


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1.1.0 by Zeynel Cebeci, a year ago

Browse source code at

Authors: Zeynel Cebeci [aut, cre] , Cagatay Cebeci [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports graphics, stats, utils, methods

Imported by inaparc.

See at CRAN