A Graph-Theoretic Approach for Visualization of Data Set Feature Association

摘要

A graph-theoretic approach is presented in this paper to visually represent feature association in data sets. This visual representation of feature association, which has been named as Feature Association Map (FAM), is based on similarity between features measured using pair-wise Pearson's product moment correlation coefficient. Highly similar features will appear as clusters in the graph visualization. Data sets with high number of features as part of feature clusters will indicate the possibility of strong feature association. The efficacy of this method has been demonstrated in ten publicly available data sets. FAM can be applied effectively in the area of feature selection.