Quantitative and Ordinal Association Rules Mining (QAR Mining)

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A00121132" target="_blank" >RIV/68407700:21230/06:00121132 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Quantitative and Ordinal Association Rules Mining (QAR Mining)
Original language description
Association rules have exhibited an excellent ability to identify interesting association relationships among a set of binary variables describing huge amount of transactions. Although the rules can be relatively easily generalized to other variable types, the generalization can result in a computationally expensive algorithm generating a prohibitive number of redundant rules of little significance. This danger especially applies to quantitative and ordinal variables. This paper presents and verifies analternative approach to the quantitative and ordinal association rule mining. In this approach, quantitative or ordinal variables are not immediately transformed into a set of binary variables. Instead, it applies simple arithmetic operations in order to construct the cedents and searches for areas of increased association which are finally decomposed into conjunctions of literals. This scenario outputs rules that do not syntactically differentiate from classical association rules.
Czech name
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Czech description
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Publication year
2006
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Knowledge-Based Intelligent Information and Engineering Systems, Part I
ISBN
3-540-46535-9
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Bournemouth
Event date
Oct 9, 2006
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000242122000024

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