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Local and Global Approximations for Incomplete Data
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11539827_11openalex-snapshot
Rough Set Strategies to Data with Missing Attribute Values
inferredcites →https://doi.org/10.1007/11548669_26openalex-snapshot
Incomplete Data and Generalization of Indiscernibility Relation, Definability, and Approximations
inferredcites →https://doi.org/10.1007/11548669_34openalex-snapshot
Rough Sets Handling Missing Values Probabilistically Interpreted
inferredcites →https://doi.org/10.1007/11574798_4openalex-snapshot
Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation
inferredcites →https://doi.org/10.1007/3-540-39205-x_6openalex-snapshot
On Generalizing Rough Set Theory
inferredcites →https://doi.org/10.1007/3-540-54563-8_100openalex-snapshot
On the unknown attribute values in learning from examples
inferredcites →https://doi.org/10.1007/978-1-4757-4919-9_20openalex-snapshot
Dealing with Missing Data in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems
inferredcites →https://doi.org/10.1007/978-3-540-25929-9_29openalex-snapshot
Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation
inferredcites →https://doi.org/10.1007/978-3-540-27794-1_3openalex-snapshot
Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction
inferredcites →https://doi.org/10.1007/978-3-540-48061-7_11openalex-snapshot
On the Extension of Rough Sets under Incomplete Information
inferredcites →https://doi.org/10.1007/978-3-540-85064-9_2openalex-snapshot
Local and Global Approximations for Incomplete Data
inferredcites →https://doi.org/10.1007/978-94-015-7975-9_18openalex-snapshot
Topological and Fuzzy Rough Sets
inferredcites →https://doi.org/10.1007/978-94-015-7975-9_21openalex-snapshot
The Discernibility Matrices and Functions in Information Systems
inferredcites →https://doi.org/10.1016/s0020-0255(98)10006-3openalex-snapshot
Relational interpretations of neighborhood operators and rough set approximation operators
inferredcites →https://doi.org/10.1016/s0020-0255(98)10019-1openalex-snapshot
Rough set approach to incomplete information systems
inferredcites →https://doi.org/10.1016/s0020-0255(98)10065-8openalex-snapshot
Rules in incomplete information systems
inferredcites →https://doi.org/10.1016/s0888-613x(96)00071-0openalex-snapshot
Two views of the theory of rough sets in finite universes
inferredcites →https://doi.org/10.1080/10798587.1996.10750660openalex-snapshot
Generalization of Rough Sets using Modal Logics
inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/69.842271openalex-snapshot
A generalized definition of rough approximations based on similarity
inferredcites →https://doi.org/10.1109/csac.1989.81064openalex-snapshot
Chinese wall security policy-an aggressive model
inferredcites →https://doi.org/10.1109/fuzz.2002.1006657openalex-snapshot
Extension of rough set under incomplete information systems
inferredcites →https://doi.org/10.1109/icsmc.2004.1400837openalex-snapshot
Learning coverage rules from incomplete data based on rough sets
inferredcites →https://doi.org/10.1111/0824-7935.00162openalex-snapshot
Incomplete Information Tables and Rough Classification
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Rule Set Complexity in Mining Incomplete Data Using Global and Saturated Probabilistic Approximations
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Complexity of Rule Sets Induced from Data with Many Lost Values and “Do Not Care” Conditions
inferred← citeshttps://doi.org/10.1007/978-3-030-59506-7_7openalex-snapshot
Mining Data with Many Missing Attribute Values Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets
inferred← citeshttps://doi.org/10.1007/978-3-030-61705-9_32openalex-snapshot
Global and Saturated Probabilistic Approximations Based on Generalized Maximal Consistent Blocks
inferred← citeshttps://doi.org/10.1007/978-3-030-87334-9_1openalex-snapshot
Mining Incomplete Data Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets and Maximal Consistent Blocks
inferred← citeshttps://doi.org/10.1007/978-3-030-96318-7_19openalex-snapshot
Rule Set Complexity for Mining Incomplete Data Using Probabilistic Approximations Based on Generalized Maximal Consistent Blocks
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A Comparison of Two Versions of the MLEM2 Rule Induction Algorithm Extended to Probabilistic Approximations
inferred← citeshttps://doi.org/10.1007/978-3-319-25754-9_1openalex-snapshot
A Rough Set Approach to Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-319-59060-8_3openalex-snapshot
Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-319-60837-2_39openalex-snapshot
Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-319-60840-2_14openalex-snapshot
Rough Set Approximations in an Incomplete Information Table
inferred← citeshttps://doi.org/10.1007/978-3-319-99972-2_26openalex-snapshot
A Comparison of Concept and Global Probabilistic Approximations Based on Mining Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-540-71663-1_11openalex-snapshot
Lower and Upper Approximations in Data Tables Containing Possibilistic Information
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The Art of Granular Computing
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RSGUI with Reverse Prediction Algorithm
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Local and Global Approximations for Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-642-05201-9_16openalex-snapshot
Increasing Data Set Incompleteness May Improve Rule Set Quality
inferred← citeshttps://doi.org/10.1007/978-3-642-11164-8_16openalex-snapshot
Interactive Rough-Granular Computing in Pattern Recognition
inferred← citeshttps://doi.org/10.1007/978-3-642-11479-3_2openalex-snapshot
Definability and Other Properties of Approximations for Generalized Indiscernibility Relations
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A Comparison of Some Rough Set Approaches to Mining Symbolic Data with Missing Attribute Values
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Mining Incomplete Data—A Rough Set Approach
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Mining Incomplete Data—A Rough Set Approach
inferred← citeshttps://doi.org/10.1007/978-3-642-34624-8_11openalex-snapshot
Local Probabilistic Approximations for Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-642-41299-8_5openalex-snapshot
Generalizations of Approximations
inferred← citeshttps://doi.org/10.1007/978-3-642-54756-0_7openalex-snapshot
Algorithms for Similarity Relation Learning from High Dimensional Data
inferred← citeshttps://doi.org/10.1007/978-981-13-8311-3_15openalex-snapshot
Mining Incomplete Data—A Comparison of Concept and New Global Probabilistic Approximations
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Generalized rough set models determined by multiple neighborhoods generated from a similarity relation
inferred← citeshttps://doi.org/10.1007/s00521-016-2711-zopenalex-snapshot
RETRACTED ARTICLE: Hybrid rough-bijective soft set classification system
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A novel feature selection method and its application
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A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations
inferred← citeshttps://doi.org/10.1007/s10992-010-9163-1openalex-snapshot
A Logic for Multiple-source Approximation Systems with Distributed Knowledge Base
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An update logic for information systems
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Approximations and uncertainty measures in incomplete information systems
inferred← citeshttps://doi.org/10.1016/j.ins.2018.04.025openalex-snapshot
Characteristic sets and generalized maximal consistent blocks in mining incomplete data
inferred← citeshttps://doi.org/10.1016/j.procs.2020.09.219openalex-snapshot
Complexity of Rule Sets Mined from Incomplete Data Using Probabilistic Approximations Based on Generalized Maximal Consistent Blocks
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Cybersecurity in Active and Healthy Ageing Era
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Global and saturated probabilistic approximations based on generalized maximal consistent blocks
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Logics for some dynamic spaces-I
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Definability of Approximations for a Generalization of the Indiscernibility Relation
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Generalized rough set model based on the intersection of neighborhoods
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A Comparison of Three Approximation Strategies for Incomplete Data Sets
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Mining data with numerical attributes and missing attribute values — A rough set approach
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A Comparison of global and local probabilistic approximations in mining data with many missing attribute values
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Logics for information systems and their dynamic extensions
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Fuzzy Set‐Valued Information Systems and the Algorithm of Filling Missing Values for Incomplete Information Systems
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Core Reduct Based Preprocessing Approach to Incomplete Data
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A Local Version of the MLEM2 Algorithm for Rule Induction
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Application of Reduction of the Set of Conditional Attributes in the Process of Global Decision-making
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Approximation Space and LEM2-like Algorithms for Computing Local Coverings
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Rough set based pose invariant face recognition with mug shot images
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Handling the Complexity of Computing Maximal Consistent Blocks
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