principal components analysis (PCA)

A mathematical procedure for reducing the complexity of multidimensional data sets, while  retaining the majority of the original variability. The first principal component accounts for the  maximum variability possible and each successive component accounts for as much of the remaining variability as possible. The technique is used in chemometrics to visualise patterns within the data and is used in pattern recognition.