Abstract
Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.
Highlights
Association rule mining [1,2] is one of the important data mining tasks that is nowadays applied to solve different kinds of problems in weak formalization fields
Since we propose associative classification methods based on database coverage, we discuss the performed research works in associative classification fields that were related to our proposed approaches
W: our approach was significantly better than compared algorithms, L: selected rule-learning algorithm significantly outperformed our algorithm, W-L: no significant difference has been detected in the comparison
Summary
Association rule mining [1,2] is one of the important data mining tasks that is nowadays applied to solve different kinds of problems in weak formalization fields. The main goal of association rule mining is to find all rules in datasets that satisfy some basic requirements, such as minimum support and minimum confidence. It was initially proposed by Agrawal [3] to solve the market basket problem in transactional datasets and it has developed to solve many other problems, such as classification, clustering, etc. Classification approaches aim to build classifiers (models) to predict the class label of a future data object. Such analysis can help to provide us with a better understanding of the data comprehensively.
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