Rough-granular computing in knowledge discovery and data mining

The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and da...

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প্রধান লেখক: Stepaniuk, Jaroslāw, 19..-
বিন্যাস: Livre numérique
ভাষা:Anglais
প্রকাশিত: Berlin, Heidelberg : Springer Berlin Heidelberg 2008.
Cham : Springer Nature
মালা:Studies in Computational Intelligence 152
অনলাইন ব্যবহার করুন:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
টীকা: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Rough Granular Computing in Knowledge Discovery and Data Mining, Texte imprimé, 9783642089725
• Rough Granular Computing in Knowledge Discovery and Data Mining, Texte imprimé, 9783540866978
• Rough Granular Computing in Knowledge Discovery and Data Mining, Texte imprimé, 9783540708001
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245 1 0 |a Rough-granular computing in knowledge discovery and data mining   |c by Jaroslāw Stepaniuk ; edited by Janusz Kacprzyk. 
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505 0 |a I: Rough Set Methodology -- Rough Sets -- Data Reduction -- II: Classification and Clustering -- Selected Classification Methods -- Selected Clustering Methods -- A Medical Case Study -- III: Complex Data and Complex Concepts -- Mining Knowledge from Complex Data -- Complex Concept Approximations -- IV: Conclusions, Bibliography and Further Readings -- Concluding Remarks. 
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520 |a The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and data mining (KDD). In the book, the KDD foundations based on the rough set approach and granular computing are discussed together with illustrative applications. In searching for relevant patterns or in inducing (constructing) classifiers in KDD, different kinds of granules are modeled. In this modeling process, granules called approximation spaces play a special rule. Approximation spaces are defined by neighborhoods of objects and measures between sets of objects. In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on dfferent levels of modeling for compound concept approximations. Calculi on such granules are used for modeling computations on granules in searching for target (sub) optimal granules and their interactions on different levels of hierarchical modeling. The methods based on the combination of granular computing, the rough and fuzzy set approaches allow for an effcient construction of the high quality approximation of compound concepts. 
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776 0 |t Rough Granular Computing in Knowledge Discovery and Data Mining  |b Texte imprimé  |z 9783540866978 
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