Open AccessCluster and Factorial Analysis Applications in Statistical Methods
Author(s) -
Ramya Nemani
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.2145
Subject(s) - cluster analysis , factor (programming language) , computer science , principal component analysis , cluster (spacecraft) , identification (biology) , data mining , procrustes analysis , similarity (geometry) , factorial , artificial intelligence , statistics , mathematics , mathematical analysis , botany , image (mathematics) , biology , programming language
Cluster analysis is a mathematical technique in Multivariate Data Analysis which indicates the proper guidelines in grouping the data into clusters. We can understand the concept with illustrated notations of cluster Analysis and various Clustering Techniques in this Research paper. Similarity and Dissimilarity measures and Dendogram Analysis will be computed as required measures for Analysis. Factor analysis technique is useful for understanding the underlying hidden factors for the correlations among the variables. Identification and isolation of such facts is sometimes important in several statistical methods in various fields. We can understand the importance of the Factor Analysis and major concept with illustrated Factor Analysis approaches. We can estimated the Basic Factor Modeling and Factor Loadings, and also Factor Rotation process. Provides the complete application process and approaches of Principal Factor M.L.Factor and PCA comparison of Factor Analysis in this Research paper