Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing [[electronic resource] ] : 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 2, 2005, Proceedings, Part II / / edited by Dominik Slezak, JingTao Yao, James F. Peters, Wojciech Ziarko, Xiaohua Hu

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005
• Edizione: 1st ed. 2005.
• Descrizione fisica: 1 online resource (XXIV, 748 p.)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Information storage and retrieval; Database management; Mathematical logic; Computers; Pattern recognition; Artificial Intelligence; Information Storage and Retrieval; Database Management; Mathematical Logic and Formal Languages; Computation by Abstract Devices; Pattern Recognition
• Persona (resp. second.): SlezakDominik; YaoJingTao; PetersJames F; ZiarkoWojciech; HuXiaohua
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Invited Papers -- Rough Set Software -- Data Mining -- Hybrid and Hierarchical Methods -- Information Retrieval -- Image Recognition and Processing -- Multimedia Applications -- Medical Applications -- Bioinformatic Applications -- Web Content Analysis -- Business Applications -- Security Applications -- Industrial Applications -- Embedded Systems and Networking -- Intelligent and Sapient Systems.
• Sommario/riassunto: This volume contains the papers selected for presentation at the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005. This conference followed in the footsteps of international events devoted to the subject of rough sets, held so far in Canada, China, Japan, Poland, Sweden, and the USA. RSFDGrC achieved the status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzis law Pawlak in 1982, is a model of approximate reasoning. The main idea is based on indiscernibility relations that describe indistinguishability of objects. Concepts are represented by - proximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas such as finance, industry, multimedia, and medicine. The RSFDGrC conferences put an emphasis on connections between rough sets and fuzzy sets, granular computing, and knowledge discovery and data mining, both at the level of theoretical foundations and real-life applications. In the case of this event, additional effort was made to establish a linkage towards a broader range of applications. We achieved it by including in the conference program the workshops on bioinformatics, security engineering, and embedded systems, as well as tutorials and sessions related to other application areas.
• Titolo autorizzato: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996465400003316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 3642