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. −−−−− 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.
Highlights
Data mining deals principally with extracting knowledge from data
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
Summary
Data mining deals principally with extracting knowledge from data. Association rule mining is concerned with extracting a set of highly correlated features shared among a large number of records in a given database. It uses unsupervised learning where no class attribute is involved in finding the association rule. Classification uses supervised learning where class attribute is involved in the construction of a classifier to predict the new instance. Both association and classification are significant and efficient data mining techniques
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