Formal Concept Analysis with Attribute Priorities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F09%3A00010944" target="_blank" >RIV/61989592:15310/09:00010944 - 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

Formal Concept Analysis with Attribute Priorities

Popis výsledku v původním jazyce

Formal concept analysis has recently been applied to data analysis, visualization, and knowledge extraction in various fields. Central to formal concept analysis is the notion of a formal concept which is a particular cluster in data. This chapter presents an extension of a basic setting of formal concept analysis. It allows a user to enter, along with the input data, his priorities regarding relative importance of attributes. Adding attribute priorities results in extraction of only those clusters in data which are compatible with the attribute priorities. The main effect is that the user is supplied with a smaller number of more relevant clusters and is thus not overwhelmed by a possibly large number of all formal concepts which logically exist in data. In this overview chapter, we present the approach and illustrative examples from marketing.

Název v anglickém jazyce

Formal Concept Analysis with Attribute Priorities

Popis výsledku anglicky

Formal concept analysis has recently been applied to data analysis, visualization, and knowledge extraction in various fields. Central to formal concept analysis is the notion of a formal concept which is a particular cluster in data. This chapter presents an extension of a basic setting of formal concept analysis. It allows a user to enter, along with the input data, his priorities regarding relative importance of attributes. Adding attribute priorities results in extraction of only those clusters in data which are compatible with the attribute priorities. The main effect is that the user is supplied with a smaller number of more relevant clusters and is thus not overwhelmed by a possibly large number of all formal concepts which logically exist in data. In this overview chapter, we present the approach and illustrative examples from marketing.

Druh

C - Kapitola v odborné knize

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

<a href="/cs/project/1ET101370417" target="_blank" >1ET101370417: Hierarchická analýza složitých dat</a><br>

Návaznosti

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

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Data Mining for Design and Marketing

ISBN

978-1-4200-7019-4

Počet stran výsledku

11

Strana od-do

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Počet stran knihy

336

Název nakladatele

Chapman & Hall/CRC

Místo vydání

Boca Raton, FL, USA

Kód UT WoS kapitoly

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