Using Copulas in Data Mining Based on the Observational Calculus
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00447829" target="_blank" >RIV/67985807:_____/15:00447829 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/15:10334228
Result on the web
<a href="http://dx.doi.org/10.1109/TKDE.2015.2426705" target="_blank" >http://dx.doi.org/10.1109/TKDE.2015.2426705</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TKDE.2015.2426705" target="_blank" >10.1109/TKDE.2015.2426705</a>
Alternative languages
Result language
angličtina
Original language name
Using Copulas in Data Mining Based on the Observational Calculus
Original language description
The objective of the paper is a contribution to data mining within the framework of the observational calculus, through introducing generalized quantifiers related to copulas. Fitting copulas to multidimensional data is an increasingly important method for analyzing dependencies, and the proposed quantifiers of observational calculus assess the results of estimating the structure of joint distributions of continuous variables by means of hierarchical Archimedean copulas. To this end, the existing theoryof hierarchical Archimedean copulas has been slightly extended in the paper: It has been proven that sufficient conditions for the function defining a hierarchical Archimedean copula to be indeed a copula, which have so far been rigorously established only for the special case of fully nested Archimedean copulas, hold in general. These conditions allow us to define three new generalized quantifiers, which are then thoroughly validated on four benchmark data sets and one data set from a
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-17187S" target="_blank" >GA13-17187S: Constructing Advanced Comprehensible Classifiers</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Knowledge and Data Engineering
ISSN
1041-4347
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
2851-2864
UT code for WoS article
000361245300020
EID of the result in the Scopus database
2-s2.0-84941569975