Associative Classification in R: arc, arulesCBA, and rCBA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00337444" target="_blank" >RIV/68407700:21240/19:00337444 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31140/19:00054581

Výsledek na webu

<a href="https://doi.org/10.32614/RJ-2019-048" target="_blank" >https://doi.org/10.32614/RJ-2019-048</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32614/RJ-2019-048" target="_blank" >10.32614/RJ-2019-048</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Associative Classification in R: arc, arulesCBA, and rCBA

Popis výsledku v původním jazyce

Several methods for creating classifiers based on rules discovered via association rule mining have been proposed in the literature. These classifiers are called associative classifiers and the best-known algorithm is Classification Based on Associations (CBA). Interestingly, only very few implementations are available and, until recently, no implementation was available for R. Now, three packages provide CBA. This paper introduces associative classification, the CBA algorithm, and how it can be used in R. A comparison of the three packages is provided to give the potential user an idea about the advantages of each of the implementations. We also show how the packages are related to the existing infrastructure for association rule mining already available in R.

Název v anglickém jazyce

Associative Classification in R: arc, arulesCBA, and rCBA

Popis výsledku anglicky

Several methods for creating classifiers based on rules discovered via association rule mining have been proposed in the literature. These classifiers are called associative classifiers and the best-known algorithm is Classification Based on Associations (CBA). Interestingly, only very few implementations are available and, until recently, no implementation was available for R. Now, three packages provide CBA. This paper introduces associative classification, the CBA algorithm, and how it can be used in R. A comparison of the three packages is provided to give the potential user an idea about the advantages of each of the implementations. We also show how the packages are related to the existing infrastructure for association rule mining already available in R.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

The R Journal

ISSN

2073-4859

e-ISSN

2073-4859

Svazek periodika

11

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

14

Strana od-do

254-267

Kód UT WoS článku

000522733900018

EID výsledku v databázi Scopus

2-s2.0-85087025613