Mining incomplete data using global and saturated probabilistic approximations based on characteristic sets and maximal consistent blocks

DOI10.1007/978-3-030-87334-9_1zbMath1495.68192OpenAlexW3199353390MaRDI QIDQ2670885

Teresa Mroczek, Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe, Patrick G. Clark

Publication date: 1 June 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-87334-9_1

zbMATH Keywords

data miningrough set theorycharacteristic setsprobabilistic approximationsmaximal consistent blocks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Uses Software

• LERS

Cites Work

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• Rough sets: some extensions
• Probabilistic rough set approximations
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• Maximal consistent block technique for rule acquisition in incomplete information systems.
• Characteristic sets and generalized maximal consistent blocks in mining incomplete data
• Generalized probabilistic approximations of incomplete data
• The investigation of the Bayesian rough set model
• Rough sets: probabilistic versus deterministic approach
• Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks
• Local and Global Approximations for Incomplete Data
• Local and Global Approximations for Incomplete Data

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