Clustering algorithms based on sampling

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Clustering algorithms based on sampling

Popis výsledku v původním jazyce

This paper deals with the description and the comparison of selected algorithms for clustering large datasets. A common feature of observed algorithms is attempt to decrease of time-consuming process of clustering by reducing the number of passages through the data file and by using different sampling methods. In some cases, sampling is done one-time passage of a data file in which they are constructed trees of various types (R *-trees, CF- trees). Using the resulting tree is created a file representingthe structure of the original data file, but with much smaller number of objects. In other cases, the data sample chosen at random. Custom clustering already takes place only within this sample file. When further steps are created clusters are modifiedwithin one or a few passages through the original file.

Název v anglickém jazyce

Clustering algorithms based on sampling

Popis výsledku anglicky

This paper deals with the description and the comparison of selected algorithms for clustering large datasets. A common feature of observed algorithms is attempt to decrease of time-consuming process of clustering by reducing the number of passages through the data file and by using different sampling methods. In some cases, sampling is done one-time passage of a data file in which they are constructed trees of various types (R *-trees, CF- trees). Using the resulting tree is created a file representingthe structure of the original data file, but with much smaller number of objects. In other cases, the data sample chosen at random. Custom clustering already takes place only within this sample file. When further steps are created clusters are modifiedwithin one or a few passages through the original file.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Informační Bulletin České statistické společnosti

ISSN

1210-8022

e-ISSN

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Svazek periodika

23

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

10-19

Kód UT WoS článku

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EID výsledku v databázi Scopus

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