Correlation analysis is an effective mechanism for studying patterns in data and making predictions.
Many interesting discoveries have been made by formulating correlations in seemingly unrelated data.  We propose
an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.
We propose a predictive metric to calculate correlations between paired values, known as the general rank-based
correlation  coefficient.   It  fulfills  the  five  basic  criteria  of  a  predictive  metric:   independence  from  sample  size,
value between ? 1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.
Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number
simulations.  Mathematical derivations of the proposed equations have also been provided. We have compared it to
Spearman’s rank correlation coefficient. The comparison results show that the proposed metric fares better than the
existing metric on all the predictive metric criteria.

关键词： General rank-based correlation coefficient,  Multivariate analysis,  Predictive metric,  Spearman’s rank
correlation coefficient