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.

Associative classification method applies association rule mining technique in classification and achieves higher classification accuracy. However, it is a known fact that associative classification typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence, generating, ranking and selecting a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but indeed a challenging task. Lazy learning associative classification method eliminates the need of constructing the classifier but suffers with high computation cost. This paper proposes lazy associative classification using Information gain where, the system first chooses the Information gained attribute from the training sample and computes highest subset probability and then it directly predicts the class label. This proposed method not only reduces the computation cost but also improves the classification accuracy. Experimental result shows that the proposed system outperforms the traditional associative classification methods and the existing lazy associative classification method.