Year: 2024
Type: article
Abstract: Association Rules (AR) are widely used for data mining in industry, and have been extensively researched in academia. An elementary step in the calculation of the strength of each proposed rule X -> Y is the tabulation of occurrences and co-occurrences of X and Y. Yet, a fundamental question does not appear to have received attention in the literature. The question is, how should one count these occurrences? Nearly all researchers and practitioners use one method, but there is actually an alternative way to count, and the data mining literature has not seriously considered the alternative or justified the prevailing choice. This fundamental question of counting is not a purely theoretical difference; the methods yield different results. In this research, we investigate the implications of the two methods. Results include the following: (1) Both methods can be correct under a different probabilistic setup; (2) The two counting methods yield different results, in terms of the relative order of rules when ranked by strength; (3) The extent to which the methods diverge depends on properties of the data, one of which we identify; (4) The methods can be compared based on their properties, one of which we investigate. The contribution of our work is that it brings to light a technical choice that impacts results, and provides a few bases upon which a researcher or practitioner can make a principled choice of which method to use when employing AR.
Authors: David Bodoff, Marina Feldus Goldman
Institution: University of Haifa
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: University of Haifa
OpenAlex ID: W4405681008