<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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 oneelement clusters—allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.</p></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 oneelement clusters—allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.

A complete gradient clustering algorithm formed with kernel estimators

Systems Research Institute, Center for Stochastic Data Analysis Methods, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

Department of Automatic Control and Information Technology, Faculty of Electrical and Computer Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland

Institute of Mathematics and Computer Science, John Paul II Catholic University of Lublin, ul. Konstantynów 1 H, 20-708 Lublin, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"A complete gradient clustering algorithm formed with kernel estimators"}

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...

A complete gradient clustering algorithm formed with kernel estimators

Data publikacji: 25 mar 2010

Zakres stron: 123 - 134

DOI: https://doi.org/10.2478/v10006-010-0009-3

Słowa kluczowe
data analysis and mining, clustering, gradient procedures, nonparametric statistical methods, kernel estimators, numerical calculations

This content is open access.

A complete gradient clustering algorithm formed with kernel estimators

Data publikacji: 25 mar 2010

Zakres stron: 123 - 134

DOI: https://doi.org/10.2478/v10006-010-0009-3

Słowa kluczowedata analysis and mining, clustering, gradient procedures, nonparametric statistical methods, kernel estimators, numerical calculations

This content is open access.

Słowa kluczowe
data analysis and mining, clustering, gradient procedures, nonparametric statistical methods, kernel estimators, numerical calculations