Building an associative classifier with multiple minimum supports

• Published in: SpringerPlus Vol. 5; no. 1; p. 528
• Main Authors: Hu, Li-Yu, Hu, Ya-Han, Tsai, Chih-Fong, Wang, Jian-Shian, Huang, Min-Wei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 26.04.2016; Springer Nature B.V
• Subjects: Classification; Computer Science; Data mining; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary); Multiple minimum supports; Data mining; Association rule; Classification based on associations
• ISSN: 2193-1801; 2193-1801
• DOI: 10.1186/s40064-016-2153-1

Classification is one of the most important technologies used in data mining. Researchers have recently proposed several classification techniques based on the concept of association rules (also known as CBA-based methods). Experimental evaluations on these studies show that in average the CBA-based approaches can yield higher accuracy than some of conventional classification methods. However, conventional CBA-based methods adopt a single threshold of minimum support for all items, resulting in the rare item problem. In other words, the classification rules will only contain frequent items if minimum support ( minsup ) is set as high or any combinations of items are discovered as frequent if minsup is set as low. To solve this problem, this paper proposes a novel CBA-based method called MMSCBA, which considers the concept of multiple minimum supports (MMSs). Based on MMSs, different classification rules appear in the corresponding minsups . Several experiments were conducted with six real-world datasets selected from the UCI Machine Learning Repository. The results show that MMSCBA achieves higher accuracy than conventional CBA methods, especially when the dataset contains rare items.