CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification^|^sup1;

S P Syed Ibrahim, K R Chandran, C J Kabila Kanthasamy
2014 Data Science Journal  
The associative classification method integrates association rule mining and classification. Constructing an efficient classifier with a small set of high quality rules is a highly important but indeed a challenging task. The lazy learning associative classification method successfully removes the need for a classifier but suffers from high computation costs. This paper proposes a Compact Highest Subset Confidence-Based Associative Classification scheme that generates compact subsets based on
more » ... formation gain and classifies the new samples without constructing classifiers. Experimental results show that the proposed system out performs both the traditional and the existing lazy learning associative classification methods.
doi:10.2481/dsj.14-035 fatcat:iag37tsdfzb2fe5lhcgu3nt5my