http://popups.lib.uliege.be/1373-5411/index.php?id=191 Index terms fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=2266 In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, Knowledge Discovery in Databases or Data Mining has emerged as a new researcha rea. However, the approaches studied in this area have mainly been oriented at highly structured and precise data pertaining to a single dimension mostly. In addition, the goal to obtain understandable results is often neglected. Since the aim of fuzzy technology has always been to model linguistic information and to achieve understandable solutions, we expect it to play an important role in heterogeneous information mining. The objective of the paper is to analyze heterogeneous information sources with the prominent aim of producing comprehensible results. Wed, 31 Jul 2024 12:19:33 +0200 Wed, 31 Jul 2024 12:19:46 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2266 http://popups.lib.uliege.be/1373-5411/index.php?id=189 We consider the problem of discovering patterns from a given logic that are significant (i.e. interesting and sufficiently valid) with respect to a given data set. We first define two types of patterns that extend the notions of query and clause, and we propose new measures of interest and confidence. In this framework, we establish connections between our measures, first-order logic and logics of probability of Halpern. Then, we present mining algorithms based on the generic levelwise search method proposed by Mannila, and discuss implementation issues in a relational database environment. Finally, we offer concluding remarks and suggestions for future research. Wed, 19 Jun 2024 11:12:43 +0200 Wed, 19 Jun 2024 11:12:55 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=189