# A complete gradient clustering algorithm formed with kernel estimators

Piotr Kulczycki; Małgorzata Charytanowicz

International Journal of Applied Mathematics and Computer Science (2010)

- Volume: 20, Issue: 1, page 123-134
- ISSN: 1641-876X

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topPiotr Kulczycki, and Małgorzata Charytanowicz. "A complete gradient clustering algorithm formed with kernel estimators." International Journal of Applied Mathematics and Computer Science 20.1 (2010): 123-134. <http://eudml.org/doc/207968>.

@article{PiotrKulczycki2010,

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.},

author = {Piotr Kulczycki, Małgorzata Charytanowicz},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {data analysis and mining; clustering; gradient procedures; nonparametric statistical methods; kernel estimators; numerical calculations},

language = {eng},

number = {1},

pages = {123-134},

title = {A complete gradient clustering algorithm formed with kernel estimators},

url = {http://eudml.org/doc/207968},

volume = {20},

year = {2010},

}

TY - JOUR

AU - Piotr Kulczycki

AU - Małgorzata Charytanowicz

TI - A complete gradient clustering algorithm formed with kernel estimators

JO - International Journal of Applied Mathematics and Computer Science

PY - 2010

VL - 20

IS - 1

SP - 123

EP - 134

AB - 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.

LA - eng

KW - data analysis and mining; clustering; gradient procedures; nonparametric statistical methods; kernel estimators; numerical calculations

UR - http://eudml.org/doc/207968

ER -

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