document: In the recent years and mainly motivated by the impulse of data mining many methods for dimensionality reduction have arisen. Within these, it is worth highlighting the Principal Component Analysis method (PCA) (Jolliffe, 2002). In an N-dimensional vector space, the simplest version of PCA (linear PCA) is a technique that finds the mutually-uncorrelated vectors onto which the projection of the samples generates the highest variances. The result is a set of orthogonal vectors sorted in descending order of achieved variance. The first of these vectors is that onto which the variance of the projection of the samples is maximum. In this sense, the original KPIs constitute the N-dimensional vector space basis, whereas the N^ synthetic KPIs represent the orthogonal vectors with the highest variance. To be rigorous, up to N synthetic orthogonal KPIs may be computed. However, only a small set of them, the first N^, is enough to account for most of the variance of the data.
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