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 information 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. −−−−−
Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014) March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS.
Ibrahim SPS, Chandran KR, Kanthasam CJK. CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification. Data Science Journal. 2014;13:127–37. DOI: http://doi.org/10.2481/dsj.14-035
Ibrahim, S. P. S., Chandran, K. R., & Kanthasam, C. J. K. (2014). CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification. Data Science Journal, 13, 127–137. DOI: http://doi.org/10.2481/dsj.14-035
Ibrahim SPS, Chandran KR and Kanthasam CJK, ‘CHISC-AC: Compact Highest Subset Confidence-based Associative Classification’ (2014) 13 Data Science Journal 127 DOI: http://doi.org/10.2481/dsj.14-035