The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery

Many researchers in our community (this author included) regularly emphasize the role constraints play in improving performance of data-mining algorithms. This emphasis has led to remarkable progress -- current algorithms allow an incredibly rich and varied set of hidden patterns to be efficiently elicited from massive datasets, even under the burden of NP-hard problem definitions and disk-resident or distributed data. But this progress has come at a cost. In our single-minded drive towards maximum performance, we have often neglected and in fact hindered the important role of discovery in the knowledge discovery and data-mining (KDD) process. In this paper, I propose various strategies for applying constraints within algorithms for itemset and rule mining in order to escape this pitfall.1

• Type: Conference
• Year: 2004
• Where: CINQ
• Authors: Roberto J. Bayardo
• Keywords: CINQ 2004, Data-mining Algorithms, Database, NP-hard Problem Definitions, Role Constraints Play