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Formal Concept Analysis

Overview of attention for book
Cover of 'Formal Concept Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 An Invitation to Knowledge Space Theory
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    Chapter 2 Implications and Dependencies Between Attributes
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    Chapter 3 The Implication Logic of (n, k)-Extremal Lattices
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    Chapter 4 Making Use of Empty Intersections to Improve the Performance of CbO-Type Algorithms
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    Chapter 5 On the Usability of Probably Approximately Correct Implication Bases
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    Chapter 6 FCA in a Logical Programming Setting for Visualization-Oriented Graph Compression
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    Chapter 7 A Proposition for Sequence Mining Using Pattern Structures
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    Chapter 8 An Investigation of User Behavior in Educational Platforms Using Temporal Concept Analysis
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    Chapter 9 Hierarchies of Weighted Closed Partially-Ordered Patterns for Enhancing Sequential Data Analysis
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    Chapter 10 First Notes on Maximum Entropy Entailment for Quantified Implications
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    Chapter 11 Implications over Probabilistic Attributes
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    Chapter 12 On Overfitting of Classifiers Making a Lattice
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    Chapter 13 Learning Thresholds in Formal Concept Analysis
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    Chapter 14 The Linear Algebra in Extended Formal Concept Analysis Over Idempotent Semifields
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    Chapter 15 Distributed and Parallel Computation of the Canonical Direct Basis
Attention for Chapter 11: Implications over Probabilistic Attributes
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Citations

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Chapter title
Implications over Probabilistic Attributes
Chapter number 11
Book title
Formal Concept Analysis
Published by
Springer, Cham, June 2017
DOI 10.1007/978-3-319-59271-8_11
Book ISBNs
978-3-31-959270-1, 978-3-31-959271-8
Authors

Francesco Kriegel

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 50%
Other 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Mathematics 1 25%

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