Year: 2019
Type: preprint
Abstract: Association rules express implication formed relations among attributes in databases of itemsets. The apriori algorithm is presented, the basis for most association rule mining algorithms. It works by pruning away rules that need not be evaluated based on the user specified minimum support confidence. Additionally, variations of the algorithm are presented that enable it to handle quantitative attributes and to extract rules about generalizations of items, but preserve the downward closure property that enables pruning. Intertransformation of the extensions is proposed for special cases.
Source: arXiv (Cornell University)
Author: Mündler, Niels
Institution: Technical University of Munich
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2964103096