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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03105733" target="_blank" >RIV/68407700:21230/04:03105733 - 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

Efficiency-conscious Propositionalization for Relational Learning

Popis výsledku v původním jazyce

Systems aiming at discovering interesting knowledge in data, now commonly called data mining systems, are typically employed in finding patterns in a single relational table. Most of mainstream data mining tools are not applicable in the more challengingtask of finding knowledge in structured data represented by a multi-relational database. Although a family of methods known as inductive logic programming have been developed to tackle that challenge by immediate means, the idea of adapting structured data into a simpler form digestible by the wealth of AVL systems has been always tempting to data miners. To this end, we present a method based on constructing first-order logic features that conducts this kind of conversion, also known as propositionalization. It incorporates some basic principles suggested in previous research and provides significant enhancements that lead to remarkable improvements in efficiency of the feature- construction process.

Název v anglickém jazyce

Efficiency-conscious Propositionalization for Relational Learning

Popis výsledku anglicky

Systems aiming at discovering interesting knowledge in data, now commonly called data mining systems, are typically employed in finding patterns in a single relational table. Most of mainstream data mining tools are not applicable in the more challengingtask of finding knowledge in structured data represented by a multi-relational database. Although a family of methods known as inductive logic programming have been developed to tackle that challenge by immediate means, the idea of adapting structured data into a simpler form digestible by the wealth of AVL systems has been always tempting to data miners. To this end, we present a method based on constructing first-order logic features that conducts this kind of conversion, also known as propositionalization. It incorporates some basic principles suggested in previous research and provides significant enhancements that lead to remarkable improvements in efficiency of the feature- construction process.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/1K04108" target="_blank" >1K04108: Výzkum a implementace metod efektivní propozicionalizace databází</a><br>

Návaznosti

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

Rok uplatnění

2004

Kód důvěrnosti údajů

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

Název periodika

Kybernetika

ISSN

0023-5954

e-ISSN

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Svazek periodika

40

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

18

Strana od-do

275-292

Kód UT WoS článku

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EID výsledku v databázi Scopus

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