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 mining in order to escape this pitfall.
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Bayardo, R.J. (2006). The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_1
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DOI: https://doi.org/10.1007/11615576_1
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