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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

  • DOI - Digital Object Identifier

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.&nbsp; 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.&nbsp; The first one represen

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

  • 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

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

  • 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