Scaling, Granulation, and Fuzzy Attributes in Formal Concept Analysis.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F07%3A00004628" target="_blank" >RIV/61989592:15310/07:00004628 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Scaling, Granulation, and Fuzzy Attributes in Formal Concept Analysis.

Popis výsledku v původním jazyce

The present paper deals with scaling within the framework of formal concept analysis (FCA) of data with fuzzy attributes. In ordinary FCA, the input is a data table with yes/no attributes. Scaling is a process of transformation of data tables with general attributes, e.g. nominal, ordinal, etc., to data tables with yes/no attributes. This way, data tables with general attributes can be analyzed by means of FCA. We propose a new way of scaling, namely, scaling of general attributes to fuzzy attributes. After such a scaling, the data can be analyzed by means of FCA developed for data with fuzzy attributes. Compared to ordinary scaling to yes/no attributes, our scaling procedure is less sensitive to how a user defines a scale which eliminates the arbitrariness of user's definition of a scale. This is the main advantage of our approach. In addition, scaling to fuzzy attributes is appealing from the point of view of knowledge representation and is connected to Zadeh's concept of linguistic

Název v anglickém jazyce

Scaling, Granulation, and Fuzzy Attributes in Formal Concept Analysis.

Popis výsledku anglicky

The present paper deals with scaling within the framework of formal concept analysis (FCA) of data with fuzzy attributes. In ordinary FCA, the input is a data table with yes/no attributes. Scaling is a process of transformation of data tables with general attributes, e.g. nominal, ordinal, etc., to data tables with yes/no attributes. This way, data tables with general attributes can be analyzed by means of FCA. We propose a new way of scaling, namely, scaling of general attributes to fuzzy attributes. After such a scaling, the data can be analyzed by means of FCA developed for data with fuzzy attributes. Compared to ordinary scaling to yes/no attributes, our scaling procedure is less sensitive to how a user defines a scale which eliminates the arbitrariness of user's definition of a scale. This is the main advantage of our approach. In addition, scaling to fuzzy attributes is appealing from the point of view of knowledge representation and is connected to Zadeh's concept of linguistic

Druh

D - Stať ve sborníku

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F05%2F0079" target="_blank" >GA201/05/0079: Formální konceptuální analýza neurčitých a rozsáhlých dat: teorie, metody a aplikace</a><br>

Návaznosti

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

Rok uplatnění

2007

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of IEEE International Fuzzy Systems Conference

ISBN

978-1-4244-1209-9

ISSN

1544-5615

e-ISSN

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Počet stran výsledku

6

Strana od-do

918-923

Název nakladatele

IEEE Computer Society Press

Místo vydání

New York

Místo konání akce

London

Datum konání akce

23. 7. 2007

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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