The Development of the Inductive Database System VINLEN: A Review of Current Research

dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.description.abstractCurrent research on the VINLEN inductive database system is briefly reviewed and illustrated by selected results. The goal of research on VINLEN is to develop a methodology for deeply integrating a wide range of knowledge generation operators with a relational database and a knowledge base. The current system has already integrated an AQ learning system for generating attributional rules in two modes: theory formation, in which generated rules are consistent and complete with regard to data, and pattern discovery, in which generated rules represent strong patterns, not necessarily consistent or complete. It also has integrated a conceptual clustering module for splitting data into conceptual classes, and providing descriptions of those classes. Preliminary data management and knowledge visualization operators, such as the intel ligent target data generator (ITG) and concept association graph display, have also been integrated. To facilitate an easy interaction with the system, a user-oriented visual interface has been implemented. An example of results from applying VINLEN to a medical problem domain is presented to illustrate VINLEN knowledge discovery and representation capabilities.
dc.description.sponsorshipThis research was performed at the Machine Learning and Inference Laboratory at George Mason University. The laboratory's research activities are supported in part by the National Science Foundation under Grants No. IIS-9906858 and IIS-0097476, and in part by the UMBC/LUCITE #32 grant.
dc.format.extent325199 bytes
dc.identifier.citationKaufman, K. and Michalski, R. S., "The Development of the Inductive Database System VINLEN: A Review of Current Research," International Intelligent Information Processing and Web Mining Conference, Zakopane, Poland, 2003.
dc.relation.ispartofseriesP 03-1
dc.titleThe Development of the Inductive Database System VINLEN: A Review of Current Research


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