Clustering algorithms based on sampling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13510%2F12%3A43884859" target="_blank" >RIV/44555601:13510/12:43884859 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Clustering algorithms based on sampling
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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