@misc{Kulczycki_Piotr_A, author={Kulczycki, Piotr and Charytanowicz, Małgorzata}, howpublished={online}, publisher={Zielona Góra: Uniwersytet Zielonogórski}, language={eng}, 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.}, abstract={The proposed algorithm does not demandstrict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a realdata structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clustersin areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm - by the detection of oneelement clusters - allows identifying atypical elements, which enables their elimination or possible designation to biggerclusters, thus increasing the homogeneity of the data set.}, type={artykuł}, title={A complete gradient clustering algorithm formed with kernel estimators}, keywords={data analysis and mining, clustering, gradient procedures, nonparametric statistical methods, kernel estimators, numerical calculations}, }