Bisection-based Merging Algorithm for Creation of One-Sided Concept Lattices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F14%3A33151398" target="_blank" >RIV/61989592:15310/14:33151398 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SAMI.2014.6822386" target="_blank" >http://dx.doi.org/10.1109/SAMI.2014.6822386</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SAMI.2014.6822386" target="_blank" >10.1109/SAMI.2014.6822386</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bisection-based Merging Algorithm for Creation of One-Sided Concept Lattices

Popis výsledku v původním jazyce

In this paper we provide the new version of algorithm for creation model called Generalized One-Sided Concept Lattice (GOSCL). This model provides the specific fuzzy version of data analytical method based on the approach known as Formal Concept Analysis(FCA), which supports data tables containing the multiple types of attributes defined as fuzzy sets. The acquisition of the FCA models is computationally complex task and it is important to find more effective algorithms for their creation. Therefore, we have designed the algorithm for the reduction of the computation times, which is based on the simple division of input data table using bisection-based approach and then merging procedure compose the local models into one finally merged concept latticefor the complete input data. We present the illustrative experiments which prove the applicability of the presented algorithm for sparse data inputs, where it is possible to get significant decrease of computation times. More effective a

Název v anglickém jazyce

Bisection-based Merging Algorithm for Creation of One-Sided Concept Lattices

Popis výsledku anglicky

In this paper we provide the new version of algorithm for creation model called Generalized One-Sided Concept Lattice (GOSCL). This model provides the specific fuzzy version of data analytical method based on the approach known as Formal Concept Analysis(FCA), which supports data tables containing the multiple types of attributes defined as fuzzy sets. The acquisition of the FCA models is computationally complex task and it is important to find more effective algorithms for their creation. Therefore, we have designed the algorithm for the reduction of the computation times, which is based on the simple division of input data table using bisection-based approach and then merging procedure compose the local models into one finally merged concept latticefor the complete input data. We present the illustrative experiments which prove the applicability of the presented algorithm for sparse data inputs, where it is possible to get significant decrease of computation times. More effective a

Druh

D - Stať ve sborníku

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0041" target="_blank" >EE2.3.30.0041: Podpora vytváření excelentních výzkumných týmů a intersektorální mobility na Univerzitě Palackého v Olomouci II.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2014)

ISBN

978-1-4799-3442-3

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

103-108

Název nakladatele

IEEE Computer Society Press

Místo vydání

New York

Místo konání akce

Herl'any, Slovensko

Datum konání akce

23. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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