Principal Component Analysis, A Powerful Unsupervised Learning Technique

Data mining is a collection of analytical techniques to uncover new trends and patterns in massive databases. These data mining techniques stress visualization to thoroughly study the structure of data and to check the validity of the statistical model fit which leads to proactive decision making. Principal component analysis (PCA) is one of the unsupervised data mining tools used to reduce dimensionality in multivariate data. In PCA, dimensionality of multivariate data is reduced by transforming the correlated variables into linearly transformed uncorrelated variables. PCA summarizes the variation in a correlated multi-attribute to a set of uncorrelated components, each of which is a particular linear combination of the original variables. The extracted uncorrelated components are called principal components (PC) and are estimated from the eigenvectors of the covariance or correlation matrix of the original variables. Therefore, the objective of PCA is to achieve parsimony and reduce dimensionality by extracting the smallest number components that account for most of the variation in the original multivariate data and to summarize the data with little loss of information. A user-friendly SAS application utilizing the latest capabilities of SAS macro to perform PCA is presented here. Previously published diabetes screening data containing multi-attributes is used to reduce multi attributes into few dimensions.