An intuitive general rank-based correlation coefficient

作     者：Divya PANDOVE Shivani GOEL Rinkle RANI 

作者机构：Research Lab Computer Science and Engineering Department Thapar University Department of Computer Science Engineering School of Engineering and Applied SciencesBennett University Computer Science and Engineering Department Thapar University 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2018年第19卷第6期

页      面：699-711页

核心收录：

学科分类：08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：General rank-based correlation coefficient Multivariate analysis Predictive metric Spearman's rankcorrelation coefficient 

摘      要：Correlation analysis is an effective mechanism for studying patterns in data and making *** 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 *** 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 ***, 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.