Setsuo Ohsuga – författare

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

This volume contains the papers selected for presentation at the Twelfth - ternational Symposium on Methodologies for Intelligent Systems - ISMIS 2000, held in Charlotte, N.C., 11{14 October, 2000. The symposium was co-organized by the College of Information Technology at UNC-Charlotte and the Polish- Japanese Institute of Information Technology. It was sponsored by the US Army Research O ce, NCR Data Mining Laboratory, College of IT at UNC-Charlotte, and others. ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. Since then it has been held in Charlotte (North Carolina), Knoxville (Tennessee), Torino (Italy), Trondheim (Norway), Warsaw (Poland), and Zakopane (Poland). The program committee selected the following major areas for ISMIS 2000: Evolutionary Computation, Intelligent Information Retrieval, Intelligent Inf- mation Systems, Knowledge Representation and Integration, Knowledge Dis- very and Learning, Logic for Arti cial Intelligence, and Methodologies. The contributed papers were selected from 112 full draft papers by the f- lowing program committee: A. Biermann, P. Bosc, J. Calmet, S. Carberry, N. Cercone, J. Chen, W. Chu, B. Croft, J.Debenham, S.M. Deen, K. DeJong, R. Demolombe, B. Desai, T. Elomaa, F. Esposito, A. Giordana, J. Grzymala-Busse, M. Hadzikadic, H. Hamilton, D. Hislop, K. Hori, W. Kloesgen, Y. Kodrato , J. Komorowski, J. Koronacki, W. Kosinski, R. Kosto , B.G.T. Lowden, D. Maluf, D. Malerba, R.L. de Mantaras, S. Matwin, R. Meersman, Z. Michalewicz, R.

This volume contains the papers selected for presentation at the First Asia- Paci?c Conference on Web Intelligence (WI 2001) held in Maebashi TERRSA, Maebashi City,Japan,October 23-26,2001. It was sponsored by ACM SIGART and Maebashi Institute of Technology,in cooperation with ACM SIGCHI,ACM SIGWEB,the Japanese Society for Arti?cial Intelligence (JSAI),JSAI SIGFAI, JSAI SIGKBS,and IEICE SIGKBSE. The conference was held jointly with the Second Asia-Paci?c Conference on Intelligent Agent Technology (IAT 2001). WI 2001 was the ?rst conference on a new and emerging sub?eld of computer science known as Web Intelligence. It provided an international forum for re- archers and practitioners to present the state of the art in the development of Web intelligence,to examine performance characteristics of various approaches in Web-based intelligent information technology,and to cross-fertilize ideas on the development of Web-based intelligent information systems among di?erent domains.By idea-sharing and discussions on the underlying foundations and the enabling technologies of Web intelligence,we hoped to stimulate the future development of new models,new methodologies,and new tools for building a variety of embodiments of Intelligent Web Information Systems (IWIS). In spite of its name,WI 2001 was truly an international conference that - tracted 153 full-length research paper submissions from 31 countries and regions of all continents. Each submitted paper was reviewed by at least three experts on the basis of technical soundness,relevance,originality,signi?cance,and clarity.

This volume contains the papers selected for presentation at the Seventh Int- national Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (RSFDGrC'99) held in the Yamaguchi Resort Center, Ube, Y- aguchi, Japan, November 9-11, 1999. The workshop was organized by Inter- tional Rough Set Society, BISC Special Interest Group on Granular Comp- ing (GrC), Polish-JapaneseInstitute of Information Technology,and Yamaguchi University. RSFDGrC'99providedaninternationalforumforsharingoriginalresearch results and practical development experiences among experts in these emerging ?elds.An importantfeatureoftheworkshopwasto stresstheroleofthe integ- tion of intelligent information techniques. That is, to promote a deep fusion of these approaches to AI, Soft Computing, and Database communities in order to solve real world, large, complex problems concerned with uncertainty and fuz- ness. In particular, rough and fuzzy set methods in data mining and granular computing were on display. The total of 89 papers coming from 21 countries and touching a wide spectrum of topics related to both theory and applications were submitted to RSFDGrC'99.Out of them 45 papers were selected for regular presentations and 15 for short presentations. Seven technical sessions were organized, namely: Rough Set Theory and Its Applications; Fuzzy Set Theory and Its Applications; Non-Classical Logic and Approximate Reasoning; Information Granulation and Granular Computing; Data Mining and Knowledge Discovery; Machine Lea- ing; Intelligent Agents and Systems. TheRSFDGrC'99programwasenrichedbyfourinvitedspeakers:Zdzis law Pawlak, Lot? A. Zadeh, Philip Yu, and Setsuo Arikawa, from Soft Computing, Database, and AI communities. A special session on Rough Computing: Fo- dations and Applications was organized by James F. Peters.

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.