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001 978-1-4471-2760-4
003 DE-He213
005 20140220083236.0
007 cr nn 008mamaa
008 120220s2012 xxk| s |||| 0|eng d
020 _a9781447127604
_9978-1-4471-2760-4
024 7 _a10.1007/978-1-4471-2760-4
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPeters, Georg.
_eeditor.
245 1 0 _aRough Sets: Selected Methods and Applications in Management and Engineering
_h[electronic resource] /
_cedited by Georg Peters, Pawan Lingras, Dominik Ślęzak, Yiyu Yao.
264 1 _aLondon :
_bSpringer London,
_c2012.
300 _aX, 214p. 130 illus., 86 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing,
_x1610-3947
505 0 _aPreface -- Contributors -- Part I: Foundations of Rough Sets -- An Introduction to Rough Sets -- Part II: Methods and Applications in Data Analysis -- Applying Rough Set Concepts to Clustering -- Rough Clustering Approaches for Dynamic Environments -- Feature Selection, Classification and Rule Generation using Rough Sets -- Part III: Methods and Applications in Decision Support -- Three-way Decisions using Rough Sets -- Rough Set Based Decision Support – Models East to Interpret -- Part IV: Methods and Applications in Management -- Financial Series Forecasting using Dual Rough Support Vector Regression -- Grounding Information Technology Project Critical Success Factors within the Organization -- Workflow Management supported by Rough Set Concepts -- Part V: Methods and Applications in Engineering -- Rough Natural Hazards Monitoring -- Nearness of Associated Rough Sets -- Contributor's Biography -- Index.
520 _aRough Set Theory was introduced in the early 1980's. In the last quarter century it has become an important part of soft computing and has proved its relevance in many real-world applications. Initially most articles on Rough Sets were centered on theory, currently though the focus of the research has shifted to practical usage of mathematical advances. With this in mind this book is written for researchers at universities wanting to use Rough Sets to solve real-world problems and needing guidance on how best to describe their ideas in ways not only understandable to industry readers, but also for managers looking for methods to improve their businesses, and researchers in industrial laboratories and think-tanks investigating new methods to enhance the efficiency of their solutions. Rough Sets: Selected Methods and Applications in Management and Engineering is unique in its focus on use cases backed by sound theory in contrast to the presentation of a theory applied to a problem. A diverse range of applications, including coverage of methods in data analysis, decision support as well as management and engineering, demonstrates the great potential of Rough Sets in almost any domain.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aInformation systems.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputer Appl. in Administrative Data Processing.
700 1 _aLingras, Pawan.
_eeditor.
700 1 _aŚlęzak, Dominik.
_eeditor.
700 1 _aYao, Yiyu.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447127598
830 0 _aAdvanced Information and Knowledge Processing,
_x1610-3947
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-2760-4
912 _aZDB-2-SCS
999 _c100705
_d100705