Developer Guide

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Correlation and Variance-Covariance Matrices

Variance-covariance and correlation matrices are among the most important quantitative measures of a data set that characterize statistical relationships involving dependence.
Specifically, the covariance measures the extent to which variables "fluctuate together" (that is, co-vary). The correlation is the covariance normalized to be between -1 and +1. A positive correlation indicates the extent to which variables increase or decrease simultaneously. A negative correlation indicates the extent to which one variable increases while the other one decreases. Values close to +1 and -1 indicate a high degree of linear dependence between variables.
For more information on the concepts behind the algorithm, see "Details" section.
For more information on the algorithm's parameters for a specific computation mode and examples of its usage, see "Batch Processing", "Online Processing" and "Distributed Processing" sections.

Product and Performance Information

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