Computing the minimum-support for mining frequent patterns

Authors:
Shichao Zhang;Xindong Wu;Chengqi Zhang;Jingli Lu
Affiliations:
Guangxi Normal University, Faculty of Computer Science and Information Technology, 541004, Guilin, People’s Republic of China;University of Vermont, Department of Computer Science, 05405, Burlington, VT, USA;University of Technology, Sydney, Faculty of Information Technology, PO Box 123, 2007, Broadway, NSW, Australia;Massey University, Institute of Information Sciences and Technology, PO Box 123, 2007, Palmerston North, NSW, New Zealand
Venue:
Knowledge and Information Systems
Year:
2008

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Abstract

Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users’ mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements.