Tolun, M.R.Sever, H.Gorur, A.K.2025-05-132025-05-132007076953032X9780769530321https://doi.org/10.1109/GRC.2007.4403092https://hdl.handle.net/20.500.12416/9964IEEE Computational Intelligence Society (CIS)Tolun, Mehmet Resit/0000-0002-8478-7220Organizational memory in today's business world forms basis for organizational learning, which is the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures. This basically requires consideration of different aspects of knowledge that may reside on top of a conventional information management system. Of them, representation, retrieval and production issues of meta patterns constitute to the main theme of this article. Particularly we are interested in a formal approach to handle rough concepts. We utilize rough classifiers to propose a preliminary framework based on minimal term sets with p-norms to extract meta patterns. We describe a relational rule induction approach, which is called rila. Experimental results are provided on the mutagenesis, and the KDD Cup 2001 genes data sets. © 2007 IEEE.eninfo:eu-repo/semantics/closedAccessClassifierLogicOptimistic Estimate PruningP-Norm RetrievalRelational Rule InductionRough SetsRule Selection StrategiesConference Object19219810.1109/GRC.2007.44030922-s2.0-46749092989WOS:000252984500040N/AN/A