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Formal Concept Analysis

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Cover of 'Formal Concept Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Methods of Conceptual Knowledge Processing
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    Chapter 2 An Enumeration Problem in Ordered Sets Leads to Possible Benchmarks for Run-Time Prediction Algorithms
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    Chapter 3 Attribute Implications in a Fuzzy Setting
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    Chapter 4 The Assessment of Knowledge, in Theory and in Practice
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    Chapter 5 The Basic Theorem on Preconcept Lattices
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    Chapter 6 The Tensor Product as a Lattice of Regular Galois Connections
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    Chapter 7 Two Instances of Peirce’s Reduction Thesis
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    Chapter 8 Very Fast Instances for Concept Generation
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    Chapter 9 Negation, Opposition, and Possibility in Logical Concept Analysis
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    Chapter 10 A Note on Negation: A PCS-Completion of Semilattices
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    Chapter 11 Towards a Generalisation of Formal Concept Analysis for Data Mining Purposes
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    Chapter 12 Interactive Association Rules Discovery
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    Chapter 13 About the Family of Closure Systems Preserving Non-unit Implications in the Guigues-Duquenne Base
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    Chapter 14 Spatial Indexing for Scalability in FCA
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    Chapter 15 Homograph Disambiguation Using Formal Concept Analysis
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    Chapter 16 Using Concept Lattices to Uncover Causal Dependencies in Software
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    Chapter 17 An FCA Interpretation of Relation Algebra
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    Chapter 18 Spring-Based Lattice Drawing Highlighting Conceptual Similarity
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    Chapter 19 Characterizing Planar Lattices Using Left-Relations
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    Chapter 20 Automated Layout of Small Lattices Using Layer Diagrams
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    Chapter 21 Counting Pseudo-intents and #P-completeness
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8 Mendeley
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Title
Formal Concept Analysis
Published by
Springer Berlin Heidelberg, January 2006
DOI 10.1007/11671404
ISBNs
978-3-54-032203-0, 978-3-54-032204-7
Editors

Missaoui, Rokia, Schmidt, Jürg

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 13%
Malaysia 1 13%
Unknown 6 75%

Demographic breakdown

Readers by professional status Count As %
Professor 3 38%
Student > Ph. D. Student 2 25%
Lecturer > Senior Lecturer 1 13%
Student > Master 1 13%
Professor > Associate Professor 1 13%
Other 0 0%
Readers by discipline Count As %
Computer Science 7 88%
Mathematics 1 13%