2007

referat

angielski

EN Evaluating the interestingness of rules or trees is a challenging problem of knowledge discovery and data mining. In recent studies, the use of two interestingness measures at the same time was prevailing. Mining of Pareto-optimal borders according to support and confidence, or support and anti-support are examples of that approach. Here, we consider induction of “if..., then...” association rules with a fixed conclusion. We investigate ways to limit the set of rules non–dominated wrt support and confidence or support and anti-support, to a subset of truly interesting rules. Analytically, and through experiments, we show that both of the considered sets can be easily reduced by using the valuable semantics of confirmation measures.

534 - 542

10.1007/978-3-540-73451-2_56

https://link.springer.com/chapter/10.1007/978-3-540-73451-2_56

Rough Sets and Intelligent Systems Paradigms. International Conference, RSEISP 2007, Warsaw, Poland, June 28-30, 2007. Proceedings

International Conference Rough Sets and Intelligent Systems Paradigms, RSEISP 2007, 28-30.06.2007, Warszawa, Poland