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Reading: CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification

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Research Papers

CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification

Authors:

S P Syed Ibrahim ,

School of Computing Science and Engineering, VIT University - Chennai Campus, Tamilnadu, India, IN
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K R Chandran,

Department of Information Technology, PSG College of Technology, Coimbatore, Tamilnadu, India, IN
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C J Kabila Kanthasam

Department of Information Technology, Avinashilingam University for Women, Coimbatore, Tamilnadu, India, IN
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Abstract

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.
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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.

DOI: http://doi.org/10.2481/dsj.14-035
How to Cite: 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, pp.127–137. DOI: http://doi.org/10.2481/dsj.14-035
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Published on 06 Nov 2014.
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