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024 7 $a10.1007/11548706
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035   $a(DE-He213)978-3-540-31824-8
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035   $a(PPN)123097126
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100   $a20100725d2005      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aRough Sets, Fuzzy Sets, Data Mining, and Granular Computing $e10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 2, 2005, Proceedings, Part II /$fedited by Dominik Slezak, JingTao Yao, James F. Peters, Wojciech Ziarko, Xiaohua Hu
205   $a1st ed. 2005.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005.
215   $a1 online resource (XXIV, 748 p.)
225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3642
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a3-540-31824-0 
311 08$a3-540-28660-8 
320   $aIncludes bibliographical references and index.
327   $aInvited 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.
330   $aThis volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st?September 3rd, 2005. This conference followed in the footsteps of inter- tional events devoted to the subject of rough sets, held so far in Canada, China, Japan,Poland,Sweden, and the USA. RSFDGrC achievedthe status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzis law Pawlak in 1982, is a model of approximate reasoning. The main idea is based on indiscernibility relations that describe indistinguishability of objects. Concepts are represented by - proximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to signi?cant results in many areas such as ?nance, industry, multimedia, and medicine. The RSFDGrC conferences put an emphasis on connections between rough sets and fuzzy sets, granularcomputing, and knowledge discoveryand data m- ing, both at the level of theoretical foundations and real-life applications. In the case of this event, additional e?ort was made to establish a linkage towards a broader range of applications. We achieved it by including in the conference program the workshops on bioinformatics, security engineering, and embedded systems, as well as tutorials and sessions related to other application areas.
410  0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3642
606   $aArtificial intelligence
606   $aInformation storage and retrieval systems
606   $aDatabase management
606   $aMachine theory
606   $aComputer science
606   $aPattern recognition systems
606   $aArtificial Intelligence
606   $aInformation Storage and Retrieval
606   $aDatabase Management
606   $aFormal Languages and Automata Theory
606   $aTheory of Computation
606   $aAutomated Pattern Recognition
615  0$aArtificial intelligence.
615  0$aInformation storage and retrieval systems.
615  0$aDatabase management.
615  0$aMachine theory.
615  0$aComputer science.
615  0$aPattern recognition systems.
615 14$aArtificial Intelligence.
615 24$aInformation Storage and Retrieval.
615 24$aDatabase Management.
615 24$aFormal Languages and Automata Theory.
615 24$aTheory of Computation.
615 24$aAutomated Pattern Recognition.
676   $a006.3
701   $aS?le?zak$b Dominik$01755839
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484095003321
996   $aRough sets, fuzzy sets, data mining, and granular computing$94192807
997   $aUNINA