nuggets: Data Pattern Extraction Framework in R
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F24%3AA2502O4M" target="_blank" >RIV/61988987:17610/24:A2502O4M - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-68208-7_10" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-68208-7_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-68208-7_10" target="_blank" >10.1007/978-3-031-68208-7_10</a>
Alternative languages
Result language
angličtina
Original language name
nuggets: Data Pattern Extraction Framework in R
Original language description
nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and extensible toolkit for performing rule-based analyses. Both crisp (Boolean) and fuzzy data are supported. The package generates conditions in the form of elementary conjunctions, evaluates them on a dataset, and checks the induced sub-data for interesting statistical properties. A user defined function may be evaluated on generated sub-dataset, which provides a novel generality. The aim of this paper is to present that free software to the soft computing community, as the tool could be useful to both researchers and analysts in the domain of pattern mining, as, besides searching for various existing pattern types, brand new ideas may be easily implemented and evaluated within that framework.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EH22_008%2F0004583" target="_blank" >EH22_008/0004583: Research of Excellence on Digital Technologies and Wellbeing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Modeling Decisions for Artificial Intelligence
ISBN
978-3-031-68208-7
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
115-126
Publisher name
Springer Nature Switzerland
Place of publication
Cham
Event location
Tokyo
Event date
Aug 27, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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