Open AccessPrincipal Component Analysis Online Statistical Analysis Tool
Author(s) -
O.P. Sheoran,
Vinay Kumar,
Hemant Poonia,
Komal Malik
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6014.029320
Subject(s) - principal component analysis , eigenvalues and eigenvectors , scripting language , component (thermodynamics) , computer science , covariance matrix , interface (matter) , principal (computer security) , covariance , data mining , mathematics , algorithm , statistics , programming language , operating system , artificial intelligence , physics , bubble , quantum mechanics , maximum bubble pressure method , thermodynamics
An online module to deal with PCA has been developed in ASP scripting language based on Server-Client Architecture. The module produces descriptive statistics via subprogram Descriptive Stats, computes eigenvalues and eigenvector using MxEigen Jacobisub-program, order eigenvector through MxEigsrtsub-program and finally produces eigenvalues, eigenvectors, output loadings and components scores through Output Eigenval, Output Loadings, Output Scoressub-programs. A user friendly interface has been developed for entering or pasting the data, entering various parameters such as number of variables, number of observations and selection of covariance/correlation matrix. A complete procedure for how to perform principal component has also been provided in help file.