Jan Komorowski – författare

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

This volume contains papers selected for presentation at PKDD'2000, the Fourth European Conference on Principles and Practice of Knowledge Discovery in - tabases. The rst meeting was held in Trondheim, Norway, in June 1997, the second in Nantes, France, in September 1998, and the third in Prague, Czech Republic, in September 1999. PKDD 2000 was organized in Lyon, France, on 13{16 September 2000. The conference was hosted by the Equipe de Recherche en Ing enierie des Conna- sances at the Universit e Lumi ere Lyon 2. We wish to express our thanks to the sponsors of the Conference, to the University Claude Bernard Lyon 1, the INSA of Lyon, the Conseil g en eral of the R^one, the R egion Rh^one Alpes, SPSS France, AFIA, and the University of Lyon 2, for their generous support. Knowledge discovery in databases (KDD), also known as data mining, p- vides tools for turning large databases into knowledge that can be used in pr- tice.KDD has been able to grow very rapidly since its emergence a decade ago by drawing its techniques and data mining experiences from a combination of many existing research areas: databases, statistics, mathematical logic, machine learning, automated scienti c discovery, inductive logic programming, arti cial intelligence, visualization, decision science, knowledge management, and high performance computing. The strength of KDD came initially from the value - ded by the creative combination of techniques from the contributing areas.

This volume contains papers selected for presentation at theSeventh International Symposium on Methodologies forIntelligent Systems (ISMIS '93), held at the NorwegianInstitute of Technology, Trondheim, Norway, in June 1993.The volume includes six invited talks and 43 contributedpapers organized under the following headings: logic forartificial intelligence, expert systems, intelligentdatabases, approximate reasoning, constraint programming,learning and adaptive systems, methodologies, knowledgerepresentation, and manufacturing.Theinvited talks are: "On extended disjunctive logicprograms" (J. Minker, C. Ruiz), "Towards intelligentdatabases" (F. Bry), "Methodologies for knowledge-basedsoftware engineering" (M. Lowry), "Modelling of industrialsystems" (L. Ljung), "The many faces of inductive logicprogramming" (L. De Raedt, N. Lavrac), and "Systematicassessment of temporal reasoning methods for use inautonomous agents" (E. Sandewall).