Pruning in Map-Reduce Style CbO Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F20%3A73601615" target="_blank" >RIV/61989592:15310/20:73601615 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-57855-8_8" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-57855-8_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57855-8_8" target="_blank" >10.1007/978-3-030-57855-8_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pruning in Map-Reduce Style CbO Algorithms

Popis výsledku v původním jazyce

Enumeration of formal concepts is crucial in formal concept analysis. Particularly efficient for this task are algorithms from the Close-by-One family (shortly, CbO-based algorithms). State-of-the-art CbO-based algorithms, e.g. FCbO, In-Close4, and In-Close5, employ several techniques, which we call pruning, to avoid some unnecessary computations. However, the number of the formal concepts can be exponential w.r.t. dimension of the input data. Therefore, the algorithms do not scale well and large datasets become intractable. To resolve this weakness, several parallel and distributed algorithms were proposed. We propose new CbO-based algorithms intended for Apache Spark or a similar programming model and show how the pruning can be incorporated into them. We experimentally evaluate the impact of the pruning and demonstrate the scalability of the new algorithm.

Název v anglickém jazyce

Pruning in Map-Reduce Style CbO Algorithms

Popis výsledku anglicky

Enumeration of formal concepts is crucial in formal concept analysis. Particularly efficient for this task are algorithms from the Close-by-One family (shortly, CbO-based algorithms). State-of-the-art CbO-based algorithms, e.g. FCbO, In-Close4, and In-Close5, employ several techniques, which we call pruning, to avoid some unnecessary computations. However, the number of the formal concepts can be exponential w.r.t. dimension of the input data. Therefore, the algorithms do not scale well and large datasets become intractable. To resolve this weakness, several parallel and distributed algorithms were proposed. We propose new CbO-based algorithms intended for Apache Spark or a similar programming model and show how the pruning can be incorporated into them. We experimentally evaluate the impact of the pruning and demonstrate the scalability of the new algorithm.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Ontologies and Concepts in Mind and Machine

ISBN

978-3-030-57854-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

103-116

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Bolzano; Italia

Datum konání akce

18. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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