Deterministic data sampling based on neighborhood analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86096549" target="_blank" >RIV/61989100:27240/14:86096549 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-07776-5_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-07776-5_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-07776-5_6" target="_blank" >10.1007/978-3-319-07776-5_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deterministic data sampling based on neighborhood analysis

Popis výsledku v původním jazyce

The amount of large-scale real data around us is increasing in size very quickly, as is the necessity to reduce its size by obtaining a representative sample. Such sample allows us to use a great variety of analytical methods, the direct application of which on original data would be unfeasible. There are many methods used for different purposes and with different results. In this paper, we outline a simple, flexible and straightforward approach based on analyzing the nearest neighbors that is generallyapplicable. This feature is illustrated in experiments with synthetic and real-world datasets. The properties of the representative sample show that the presented approach maintains very well internal data structures (e.g. clusters and density). The keytechnical parameters of the approach are low complexity and high scalability. (C) Springer International Publishing Switzerland 2014.

Název v anglickém jazyce

Deterministic data sampling based on neighborhood analysis

Popis výsledku anglicky

The amount of large-scale real data around us is increasing in size very quickly, as is the necessity to reduce its size by obtaining a representative sample. Such sample allows us to use a great variety of analytical methods, the direct application of which on original data would be unfeasible. There are many methods used for different purposes and with different results. In this paper, we outline a simple, flexible and straightforward approach based on analyzing the nearest neighbors that is generallyapplicable. This feature is illustrated in experiments with synthetic and real-world datasets. The properties of the representative sample show that the presented approach maintains very well internal data structures (e.g. clusters and density). The keytechnical parameters of the approach are low complexity and high scalability. (C) Springer International Publishing Switzerland 2014.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 297

ISBN

978-3-319-07775-8

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

10

Strana od-do

47-56

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Shenzhen

Datum konání akce

13. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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