PRINCIPAL COMPONENTS ANALYSIS IN EXPLORATORY FUNCTIONAL DATA ANALYSIS

Autor:	Dedaa, Alaa M., Husain, Qasim N.
Rok vydání:	2022
Předmět:	Exploratory data analysis Functional data analysis Principal components analysis Reduce Dimensions
DOI:	10.5281/zenodo.7287573
Popis:	An exploratory functional tool for data analysis is presented In this paper, which is the principal components analysis (PCA) for a data set. PCA is a common technique of exploratory data analysis (EDA) to reduce the data dimensions of while maintaining the data variance set. The presented PCA is applied in this research using the statistical program R to reduce the analysis time and effort. Furthermore, using the programming in data analysis leads to have more accurate results even if the dataset comes in different sizes . A new codes has been added with some practical examples from the day life. The modified PCA for EDA gives a competed performance results in terms of time and accurate.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69cdd8975e7fb8cd3aa950e34cb1ba81 Zobrazit plný text záznamu