  This article presents GuideR, a user-guided rule induction algorithm, which
overcomes the largest limitation of the existing methods-the lack of the
possibility to introduce user's preferences or domain knowledge to the rule
learning process. Automatic selection of attributes and attribute ranges often
leads to the situation in which resulting rules do not contain interesting
information. We propose an induction algorithm which takes into account user's
requirements. Our method uses the sequential covering approach and is suitable
for classification, regression, and survival analysis problems. The
effectiveness of the algorithm in all these tasks has been verified
experimentally, confirming guided rule induction to be a powerful data analysis
tool.
