Framework for mining of association rules from data warehouse
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F08%3APU80190" target="_blank" >RIV/00216305:26230/08:PU80190 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Framework for mining of association rules from data warehouse
Original language description
In this paper, we propose a framework for association rules mining from data warehouses. This framework presents alliance between two business intelligence areas. First area is represented by data warehouse and data cube providing high quality data. Thesecond area is represented by data mining, especially association rules mining providing an additional knowledge.<br>Association rules mining on data warehouses is different from mining on relational or transactional databases, because it deals with couple of dimensions, which form conceptual hierarchies. Thus we mine multi- and inter-dimensional association rules. There are several approaches how to mine such association rules described in literature. This framework presents a novel combination of thedata cube processing - top-down (on product dimensions) and bottom-up (on domain dimensions). We presume division of dimensions on domain and product dimensions. <br>The framework works in the following steps. The first one represen
Czech name
Rámec pro dolování asociačních pravidel z datových skladů
Czech description
In this paper, we propose a framework for association rules mining from data warehouses. This framework presents alliance between two business intelligence areas. First area is represented by data warehouse and data cube providing high quality data. Thesecond area is represented by data mining, especially association rules mining providing an additional knowledge.<br>Association rules mining on data warehouses is different from mining on relational or transactional databases, because it deals with couple of dimensions, which form conceptual hierarchies. Thus we mine multi- and inter-dimensional association rules. There are several approaches how to mine such association rules described in literature. This framework presents a novel combination of thedata cube processing - top-down (on product dimensions) and bottom-up (on domain dimensions). We presume division of dimensions on domain and product dimensions. <br>The framework works in the following steps. The first one represen
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ITAT 2008
ISBN
978-80-969184-8-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
The University of Technology Košice
Place of publication
Košice
Event location
Hotel Hrebienok, Starý Smokovec
Event date
Sep 22, 2008
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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