DAC: Discriminative Associative Classification

被引:0
|
作者
Seyfi M. [1 ]
Xu Y. [1 ]
Nayak R. [1 ]
机构
[1] Data Science Discipline, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD
关键词
Class discriminative association rules; Data mining; Discriminative associative classification; Discriminative itemsets;
D O I
10.1007/s42979-023-01819-9
中图分类号
学科分类号
摘要
In this paper, discriminative associative classification is proposed as a new classification technique based on class discriminative association rules (CDARs). These rules are defined based on discriminative itemsets. The discriminative itemset is frequent in one data class and has much higher frequencies compared with the same itemset in other data classes. The CDAR is a class associative rule (CAR) in one data class that has higher support compared with the same rule in other data classes. Compared to associative classification, there are additional challenges as the Apriori property of the subset is not applicable. The proposed algorithm is designed particularly based on well-defined distinguishing characteristics of the rules, to improve the accuracy and efficiency of the classification in data classes. A novel compact prefix-tree structure is defined for holding the rules in data classes. The empirical analysis shows the effectiveness and efficiency of the proposed method on small and large real datasets. © 2023, The Author(s).
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