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Paper details
Number 1 - March 2010
Volume 20 - 2010
A complete gradient clustering algorithm formed with kernel estimators
Piotr Kulczycki, Małgorzata Charytanowicz
Abstract
The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures.
Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from
possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand
strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real
data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters
in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm—by the detection of one-element clusters—allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.
Keywords
data analysis and mining, clustering, gradient procedures, nonparametric statistical methods, kernel estimators, numerical calculations