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|a Jian, Lirong.
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|a Hybrid rough sets and applications in uncertain decision-making /
|c Lirong Jian, Sifeng Liu, Yi Lin.
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| 260 |
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|a Boca Raton, FL :
|b Auerbach Publications,
|c ©2011.
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| 300 |
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|a 1 online resource (xvii, 266 pages) :
|b illustrations
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| 336 |
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|a text
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|a Systems evaluation, prediction, and decision-making series
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| 504 |
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|a Includes bibliographical references and index.
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|a Print version record.
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|a Introduction ; Background and Significance of Soft Computing Technology; Analytical Method of Data Mining; Automatic Prediction of Trends and Behavior; Association Analysis; Cluster Analysis; Concept Description; Deviation Detection; Knowledge Discovered by Data Mining; Characteristics of Rough Set Theory and Current Status of Rough Set Theory Research Characteristics of the Rough Set Theory; Current Status of Rough Set Theory Research; Analysis with.
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|a Decision-Making; Non-Decision-Making Analysis; Hybrid of Rough Set Theory and Other Soft Technologies; Hybrid of Rough Sets and Probability Statistics; Hybrid of Rough Sets and Dominance Relation; Hybrid of Rough Sets and Fuzzy Sets; Hybrid of Rough Set and Grey System Theory; Hybrid of Rough Sets and Neural Networks Rough Set Theory ; Information Systems and Classification; Information Systems and Indiscernibility Relation; Set and Approximations of Set; Attributes Dependence and Approximation Accuracy; Quality of Approximation and Reduct; Calculation of the Reduct and Core of Information System Based on Discernable Matrix; Decision.
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|a Table and Rule Acquisition; The Attribute Dependence, Attribute Reduct, and Core; Decision Rules; Use the Discernibility Matrix to Work Out Reducts, Core,
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|a Set Bayes' Probability; Consistent Degree, Coverage,
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| 505 |
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|a Preferential Probabilistic Decision Rules; Algorithm Design; An Application Case; Post Evaluation of Construction Projects Based on Dominance-Based Rough Set Construction of Preferential Evaluation Decision Table; Search of Reduct and Establishment of Preferential Rules; Performance Evaluation of Discipline Construction in Teaching-Research Universities Based on Dominance-Based Rough Set The Basic Principles of the Construction of Evaluation Index System The Establishment of Index System and Determination of Weight and Equivalent; Data Collection and.
|
| 650 |
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|a Soft computing.
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|a Data mining.
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|a Rough sets.
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|a Decision making.
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2 |
|a Data Mining
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2 |
|a Decision Making
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6 |
|a Informatique douce.
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| 650 |
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6 |
|a Exploration de données (Informatique)
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| 650 |
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6 |
|a Ensembles approximatifs.
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|a Prise de décision.
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|a decision making.
|2 aat
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|a COMPUTERS
|x Enterprise Applications
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|a Rough sets
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| 650 |
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|a Soft computing
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| 655 |
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|a Electronic books.
|2 local
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| 700 |
1 |
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|a Liu, Sifeng.
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| 700 |
1 |
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|a Forrest, Jeffrey Yi-Lin,
|d 1959-
|1 https://id.oclc.org/worldcat/entity/E39PBJfmvcRY3YjJ78GRy7Ytrq
|
| 710 |
2 |
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|a Taylor & Francis
|
| 758 |
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|i has work:
|a Hybrid rough sets and applications in uncertain decision-making (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCG7GHp4DFYXGPFkvxTYJKq
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
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|i Print version:
|a Jian, Lirong.
|t Hybrid rough sets and applications in uncertain decision-making.
|d Boca Raton, FL : Auerbach Publications, ©2011
|z 9781420087482
|w (DLC) 2010012957
|w (OCoLC)255885753
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|a Systems evaluation, prediction, and decision-making series.
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|u http://proxy.library.tamu.edu/login?url=https://www.taylorfrancis.com/books/9780429134654
|z Connect to the full text of this electronic book
|t 0
|
| 880 |
0 |
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|6 505-00/(S
|a Pretreatment Data Discretization; Search of Reducts and Generation of Preferential Rules; Analysis of Evaluation Results Hybrid of Rough Set Theory and Fuzzy Set Theory; The Basic Concepts of the Fuzzy Set Theory; Fuzzy Set and Fuzzy Membership Function; Operation of Fuzzy Subsets; Fuzzy Relation and Operation; Synthesis of Fuzzy Relations; λ-Cut Set and the Decomposition Proposition; The Fuzziness of Fuzzy Sets and Measure of Fuzziness; Rough Fuzzy Set and Fuzzy Rough Set; Rough Fuzzy Set; Fuzzy Rough Set; Variable Precision Rough Fuzzy.
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| 880 |
0 |
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|6 505-00/(S
|a and Support; Probability Rules; Approach to Obtain Probabilistic Rules Hybrid of Rough Set and Dominance Relation Hybrid of Rough Set and Dominance Relation; Dominance-Based Rough Set; The Classification of the Decision Tables with Preference Attribute Dominating Sets and Dominated Sets; Rough Approximation by Means of Dominance Relations; Classification Quality and Reduct; Preferential Decision Rules; Dominance-Based Variable Precision Rough Set; Inconsistency and Indiscernibility Based on Dominance Relation β-Rough Approximation Based on Dominance Relations; Classification Quality and Approximate Reduct.
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| 880 |
0 |
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|6 505-00/(S
|a and Decision Rules of Decision Table; Data Discretization; Expert Discrete Method; Equal Width Interval Method and Equal Frequency Interval Method; The Most Subdivision Entropy Method; Chimerge Method; Common Algorithms of Attribute Reduct; Quick Reduct Algorithm; Heuristic Algorithm of Attribute Reduct; Genetic Algorithm; Application Case; Data Collecting and Variable Selection; Data Discretization; Attribute Reduct; Rule Generation; Simulation of the Decision Rules Hybrid of Rough Set Theory and Probability ; Rough Membership Function; Variable Precision Rough Set Model; β-Rough.
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| 880 |
0 |
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|6 505-00/(S
|a Sets; Rough Membership Function Based on λ-Cut Set; The Rough Approximation of Variable Precision Rough Fuzzy Set The Approximate Quality and Approximate Reduct of variable Precision; &
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| 880 |
0 |
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|6 505-00/(S
|a Approximation; Classification Quality and β-Reduct; Discussion about β Value; Construction of Hierarchical Knowledge Granularity Based on VPRS Knowledge Granularity; Relationship between VPRS and Knowledge Granularity; Approximation and Knowledge Granularity; Classification Quality and Granularity Knowledge Granularity Construction of Hierarchical Knowledge Granularity; Methods of Construction of Hierarchical Knowledge Granularity Algorithm Description; Methods of Rule Acquisition Based on the Inconsistent Information System in Rough.
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