Kaufman, Kenneth A.Michalski, Ryszard S.2006-11-032006-11-032004-10Kaufman, K. and Michalski, R. S., "Initial Considerations toward Knowledge Mining," Reports of the Machine Learning and Inference Laboratory, MLI 04-4, George Mason University, Fairfax, VA, October, 2004.https://hdl.handle.net/1920/1488In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it in human-oriented forms. In efforts to satisfy this need, researchers have been exploring ideas and methods developed in machine learning, statistical data analysis, data mining, text mining, data visualization, pattern recognition, etc. The first part of this paper is a compendium of ideas on the applicability of symbolic machine learning and logical data analysis methods toward this goal. The second part outlines a multistrategy methodology for an emerging research direction, called knowledge mining, by which we mean the derivation of high-level concepts and descriptions from data through symbolic reasoning involving both data and relevant background knowledge. The effective use of background as well as previously created knowledge in reasoning about new data makes it possible for the knowledge mining system to derive useful new knowledge not only from large amounts of data, but also from limited and weakly relevant data.3083 bytes306048 bytestext/xmlapplication/pdfen-USKnowledge miningData miningInductive databasesMachine learningTechnical report