Principal Component Analysis and Factor Analysis for an Atanassov IF data set
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28150%2F21%3A63532738" target="_blank" >RIV/70883521:28150/21:63532738 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.mdpi.com/2227-7390/9/17/2067" target="_blank" >https://www.mdpi.com/2227-7390/9/17/2067</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math9172067" target="_blank" >10.3390/math9172067</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Principal Component Analysis and Factor Analysis for an Atanassov IF data set
Popis výsledku v původním jazyce
The present contribution is devoted to the theory of fuzzy sets, especially Atanassov Intu-itionistic Fuzzy sets (IF sets) and their use in practice. We define the correlation between IF sets and the correlation coefficient, and we bring a new perspective to solving the problem of data file reduc-tion in case sets where the input data come from IF sets. We present specific applications of the two best-known methods, the Principal Component Analysis and Factor Analysis, used to solve the problem of reducing the size of a data file. We examine input data from IF sets from three perspec-tives: through membership function, non-membership function and hesitation margin. This examination better reflects the character of the input data and also better captures and preserves the information that the input data carries. In the article, we also present and solve a specific example from practice where we show the behavior of these methods on data from IF sets. The example is solved using R programming language, which is useful for statistical analysis of data and their graphical representation.
Název v anglickém jazyce
Principal Component Analysis and Factor Analysis for an Atanassov IF data set
Popis výsledku anglicky
The present contribution is devoted to the theory of fuzzy sets, especially Atanassov Intu-itionistic Fuzzy sets (IF sets) and their use in practice. We define the correlation between IF sets and the correlation coefficient, and we bring a new perspective to solving the problem of data file reduc-tion in case sets where the input data come from IF sets. We present specific applications of the two best-known methods, the Principal Component Analysis and Factor Analysis, used to solve the problem of reducing the size of a data file. We examine input data from IF sets from three perspec-tives: through membership function, non-membership function and hesitation margin. This examination better reflects the character of the input data and also better captures and preserves the information that the input data carries. In the article, we also present and solve a specific example from practice where we show the behavior of these methods on data from IF sets. The example is solved using R programming language, which is useful for statistical analysis of data and their graphical representation.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
10102 - Applied mathematics
Návaznosti výsledku
Projekt
—
Návaznosti
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Mathematics
ISSN
2227-7390
e-ISSN
—
Svazek periodika
9
Číslo periodika v rámci svazku
17
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
13
Strana od-do
—
Kód UT WoS článku
000694400900001
EID výsledku v databázi Scopus
2-s2.0-85114042655