Using Copulas in Data Mining Based on the Observational Calculus

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11320/15:10334228

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Copulas in Data Mining Based on the Observational Calculus

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Using Copulas in Data Mining Based on the Observational Calculus

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Knowledge and Data Engineering

ISSN

1041-4347

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

2851-2864

Kód UT WoS článku

000361245300020

EID výsledku v databázi Scopus

2-s2.0-84941569975