Cluster Analysis And Factor Analysis

1468 Words Feb 5th, 2016 6 Pages
Introduction
Cluster analysis has many different algorithms and methods to classify objects(Saunders, 1994). One of the challenges faced by the researchers in different areas is to organize their data which is possible by cluster analysis, it is a data analysis tool which focus on classifying the different objects into groups such that the degree of association of the objects in a same group is highest if they belong and least if they do not belong. Cluster analysis is a simple term, it does not identify any statistical method or model and also there is no need to make any assumptions about distribution of data, it is used to form groups of relevant variables without providing any explanation (Stockburger, n.d.).
Despite their popularity, cluster analysis do provide a great opportunity for confusion and misapplication when compared to factor analysis, discriminant analysis and multidimensional scaling (Saunders, 1994). Both cluster analysis and factor analysis is used to organize the data into clusters or onto factors, most of the researchers who are new to this concept may feel that these two analyses are same, but they differ in many ways, the main objective of cluster analysis is to categorize the data, whereas factor analysis simplify the data, it explains the correlation in a set of data and relate variables to each other (Verial, n.d.). Cluster analysis and discriminant analysis are the two terms where we can often get confused, the basic difference between them is…
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