The Hows, Whys, and Whens of Constraints in

Abstract. 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 minin