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Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications (Hardcover, 2008 ed.) Loot Price: R3,019
Discovery Miles 30 190
Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications (Hardcover, 2008 ed.): Mikhail Ju Moshkov,...

Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications (Hardcover, 2008 ed.)

Mikhail Ju Moshkov, Marcin Piliszczuk, Beata Zielosko

Series: Studies in Computational Intelligence, 145

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Loot Price R3,019 Discovery Miles 30 190 | Repayment Terms: R283 pm x 12*

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This monograph is devoted to theoretical and experimental study of partial reductsandpartialdecisionrulesonthebasisofthestudyofpartialcovers. The use of partial (approximate) reducts and decision rules instead of exact ones allowsustoobtainmorecompactdescriptionofknowledgecontainedindecision tables, andtodesignmorepreciseclassi?ers. Weconsideralgorithmsforconstructionofpartialreductsandpartialdecision rules, boundsonminimalcomplexityofpartialreductsanddecisionrules, and algorithms for construction of the set of all partial reducts and the set of all irreducible partial decision rules. We discuss results of numerous experiments with randomly generated and real-life decision tables. These results show that partial reducts and decision rules can be used in data mining and knowledge discoverybothforknowledgerepresentationandforprediction. Theresultsobtainedinthe monographcanbe usefulforresearchersinsuch areasasmachinelearning, dataminingandknowledgediscovery, especiallyfor thosewhoareworkinginroughsettheory, testtheoryandLAD(LogicalAnalysis ofData). The monographcan be usedunder the creationofcoursesforgraduates- dentsandforPh. D. studies. An essential part of software used in experiments will be accessible soon in RSES-RoughSetExplorationSystem(InstituteofMathematics, WarsawU- versity, headofproject-ProfessorAndrzejSkowron). We are greatly indebted to Professor Andrzej Skowron for stimulated d- cussionsand varioussupportof ourwork. We aregratefulto ProfessorJanusz Kacprzykforhelpfulsuggestions. Sosnowiec, Poland MikhailJu. Moshkov April2008 MarcinPiliszczuk BeataZielosko Contents Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 PartialCovers, ReductsandDecisionRules . . . . . . . . . . . . . . . . 7 1. 1 PartialCovers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1. 1. 1 MainNotions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1. 1. 2 Known Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1. 1. 3 PolynomialApproximateAlgorithms. . . . . . . . . . . . . . . . . . 10 1. 1. 4 Bounds on C (?)Based on Information about min GreedyAlgorithm Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1. 1. 5 UpperBoundon C (?). . . . . . . . . . . . . . . . . . . . . . . . . . 17 greedy 1. 1. 6 Covers fortheMostPartofSetCoverProblems. . . . . . . . 18 1. 2 PartialTests and Reducts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1. 2. 1 MainNotions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1. 2. 2Relationships betweenPartialCovers and Partial Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1. 2. 3 PrecisionofGreedyAlgorithm. . . . . . . . . . . . . . . . . . . . . . . 24 1. 2. 4 PolynomialApproximateAlgorithms. . . . . . . . . . . . . . . . . . 25 1. 2. 5 Bounds on R (?)Based on Information about min GreedyAlgorithm Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1. 2. 6 UpperBoundon R (?). . . . . . . . . . . . . . . . . . . . . . . . . . 28 greedy 1. 2. 7 Tests fortheMostPartofBinaryDecisionTables. . . . . . 29 1. 3 PartialDecision Rules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Studies in Computational Intelligence, 145
Release date: February 2009
First published: November 2008
Authors: Mikhail Ju Moshkov • Marcin Piliszczuk • Beata Zielosko
Dimensions: 235 x 155 x 11mm (L x W x T)
Format: Hardcover
Pages: 152
Edition: 2008 ed.
ISBN-13: 978-3-540-69027-6
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > General
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LSN: 3-540-69027-1
Barcode: 9783540690276

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