A multiple association-based unsupervised feature selection algorithm for mixed data sets

Ayman Taha, Ali S. Hadi, Bernard Cosgrave, Susan McKeever

Abstract: Highlights•New generic multiple association measure for categorical, numerical and mixed data.•Proposing two algorithms to select features based on the new multiple association.•Conducting an empirical evaluation of proposed solutions to assess their traits.•Our approaches cater for unsupervised feature section datasets in mixed datasets.•Reducing the processing time required for unsupervised feature selection problem.