Abstract
Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).
Highlights
Nowadays, there a number of classification techniques that have been applied to various real-world applications, i.e., graph convolutional networks for text classification [1], automated classification of epileptic electroencephalogram (EEG) signals [2], iris cognition [3], and anomaly detection [4]
Associative classification (AC) focuses on finding Class Association Rules (CARs) that satisfy certain minimum support and confidence thresholds in the form x → c, where x is a set of attribute values and c is a class label
After the extended CAR is added to the classifier, the transaction IDs associated with the CAR will be removed
Summary
There a number of classification techniques that have been applied to various real-world applications, i.e., graph convolutional networks for text classification [1], automated classification of epileptic electroencephalogram (EEG) signals [2], iris cognition [3], and anomaly detection [4]. AC focuses on finding Class Association Rules (CARs) that satisfy certain minimum support and confidence thresholds in the form x → c, where x is a set of attribute values and c is a class label. APR can be used to avoid generating all CARs. the exhaustive search for finding rules in classifiers may cause an issue in large datasets or low minimum support. A new algorithm is proposed to directly generate a small number of efficient CARs for classification. Whenever a CAR with 100% confidence is found, the transaction associated with the CAR will be removed by using a set difference to avoid generating redundant CARs. a compact classifier is built for classification. To avoid pruning and sorting processes, the proposed algorithm directly generates CARs with.
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