دانلود کتاب Complex Data Analytics with Formal Concept Analysis

فهرست مطالب :

Foreword
Preface
Acknowledgments
Contents
List of Contributors
Acronyms
1 Formal Concept Analysis and Extensions for Complex DataAnalytics
1.1 Introduction
1.2 Background
1.2.1 Formal Concepts and Line Diagrams
1.2.2 Non Binary Data
1.2.2.1 Many-Valued Contexts
1.2.2.2 Pattern Structures
1.2.3 Implication Computation
1.3 Extensions to FCA
1.3.1 Logical FCA
1.3.2 Fuzzy FCA
1.3.3 Relational Concept Analysis
1.3.4 Triadic Concept Analysis
1.3.5 Approximation
1.4 Complex Data Analytics
1.5 Contributions
References
2 Conceptual Navigation in Large Knowledge Graphs
2.1 Introduction
2.2 Graph-FCA: Extending FCA to Knowledge Graphs
2.2.1 Graph Context
2.2.2 Graph Patterns
2.2.3 Graph Concepts
2.2.4 Graph Concept Lattice
2.3 Conceptual Navigation in Graph-FCA Lattices
2.3.1 Abstract Conceptual Navigation (ACN)
2.3.2 Graph-ACN: Instantiating ACN to Knowledge Graphs
2.4 Scaling to Large RDF Graphs with SPARQL Endpoints
2.4.1 From Graph-FCA to RDF and SPARQL
2.4.2 Computing the Result, Index, and Links
2.4.3 Living with Partial Results
2.5 Rising in Expressivity
2.5.1 An Algebraic Form of Queries
2.5.2 Extensions of the Query Algebra
2.6 The Sparklis Tool and Application Cases
2.6.1 Sparklis
2.6.2 Application Cases
2.7 Conclusion and Perspectives
References
3 FCA2VEC: Embedding Techniques for Formal Concept Analysis
3.1 Introduction
3.2 Related Work
3.3 Foundations
3.3.1 Formal Concept Analysis
3.3.2 Word2Vec
3.3.2.1 The Skip-Gram and the Continuous Bag of Words Architecture
3.4 Modeling
3.4.1 Retrieving FCA Features Through Closure2Vec
3.4.1.1 Exact Representation of the Closure Operator
3.4.1.2 Considering the Unconstraint Problem
3.4.1.3 Representing Closure Operators Using Linear Functions
3.4.1.4 Linear Representable Part of Closure Operators
3.4.1.5 Non-linear Embedding Through Closure2Vec
3.4.2 Object2Vec and Attribute2Vec
3.4.2.1 SG and CBOW in the Realm of Object2Vec
3.5 Experiments
3.5.1 Object2Vec and Attribute2Vec
3.5.1.1 Link Prediction Using Object2Vec
3.5.1.2 Clustering Attributes with Attribute2Vec
3.5.1.3 Discussion
3.5.2 FCA Features Through Closure2Vec
3.5.2.1 Distance of Covering Relation
3.5.2.2 Distance of Canonical Bases
3.5.2.3 Discussion
3.5.2.4 Empirical Structural Observations
3.6 Conclusion
References
4 Analysis of Complex and Heterogeneous Data Using FCA and Monadic Predicates
4.1 Introduction
4.2 The NextPriorityConcept Algorithm
4.2.1 Formal Concept Analysis
4.2.1.1 Concept Lattice
4.2.1.2 Pattern Structures
4.2.2 NextPriorityConcept
4.2.2.1 Predicates for Heterogeneous Data
4.2.2.2 Descriptions and Strategies
4.2.2.3 Bordat\'s Algorithm, Priority Queue and Propagation of Constraints
4.2.2.4 Description of the Algorithm
4.2.2.5 The GALACTIC Platform
4.3 Use Cases
4.3.1 Binary and Categorical Characteristics with the Lenses Dataset
4.3.1.1 Lenses with the Entropy as Strategy
4.3.1.2 Lenses with the Minimal Logic Formulae as Description
4.3.2 Numerical Characteristics with the Iris Dataset
4.3.2.1 Iris with the Entropy as Strategy.
4.3.2.2 Iris with the Convex Hull as Description
4.3.3 Sequential Characteristics with the Daily-actions Dataset
4.3.3.1 Daily-actions with the Common Subsequences
4.3.4 Sequential Characteristics with the Wine City Dataset
4.3.4.1 Wine City Dataset with the Prefix Description and Strategy
4.4 Conclusion
References
5 Dealing with Large Volumes of Complex Relational Data UsingRCA
5.1 Introduction
5.2 Background
5.3 Related Work
5.4 RCA for Environmental Data
5.4.1 Two Complex Datasets from the EnvironmentalDomain
5.4.1.1 Pesticidal and Antimicrobial Data
5.4.1.2 Water Data
5.4.2 Experimenting RCA Algorithms
5.4.2.1 Experiments on Knomana Dataset
5.4.2.2 Experiments on Fresqueau Dataset
5.4.3 Discussion
5.5 Analysing Sequences from Water Quality Monitoring UsingRCA
5.5.1 RCA-Seq
5.5.1.1 Modelling Hydro-Ecological Sequential Data.
5.5.1.2 Exploring Hydro-Ecological Sequential Data with RCA
5.5.1.3 Extracting DAG by Navigating the RCA Output
5.5.2 Experiments
5.5.3 Navigating the Resulting Hierarchy of Graphs
5.6 Conclusion
References
6 Computing Dependencies Using FCA
6.1 Introduction
6.2 Notation
6.2.1 Equivalence Relation
6.2.2 Tolerance Relations
6.3 FCA and Database Dependencies
6.3.1 Functional Dependencies
6.3.2 Similarity Dependencies
6.3.3 Formal Concept Analysis
6.3.4 Functional Dependencies as Implications
6.3.5 Pattern Structures
6.4 Results
6.4.1 Characterization of Functional Dependencies with Pattern Structures
6.4.2 Similarity Dependencies
6.5 Discussion
6.6 Conclusions
References
7 Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval
7.1 Introduction
7.2 Related Work
7.3 FCA-Based Query Expansion
7.3.1 Patterns Discovery
7.4 Patterns and Word Embeddings Based Query Expansion
7.4.1 Word Embeddings: Word2Vec Model
7.4.2 Expansion Terms Selection
7.5 Experiments
7.5.1 Dataset Description
7.5.2 Retrieval Model
7.5.3 Experimental Protocol
7.5.4 Experimental Results
7.6 Conclusion
References
8 Scalable Visual Analytics in FCA
8.1 Introduction
8.1.1 Scalable Visual Analytics in FCA
8.1.2 Organisation
8.2 Graph-Theoretic Introduction to FCA
8.2.1 Formal Context
8.2.2 Formal Concepts
8.2.3 Concept Lattice Digraph
8.2.4 Line Diagram
8.2.5 Simplifying Implications
8.2.6 Visualising Implications
8.2.7 Coordinating Views of Implications and Concepts
8.3 Introduction to Visual Analytics
8.3.1 Algorithmic Analysis
8.3.2 Graph Drawing
8.3.3 Information Visualisation
8.3.4 Multiple Coordinated Views
8.3.5 Tight Coupling
8.4 Layout, Visualisation and Interaction
8.4.1 Reducing Digraph Size
8.4.2 Layout of Line Diagram
8.4.3 Interactive Visualisation
8.4.4 Discovering or Imposing Tree Structure
8.4.5 Demand for Enhanced Tool Support
8.4.6 Implications
8.5 Three FCA Prototypes
8.5.1 Hierarchical Parallel Decomposition
8.5.2 User-Guided FCA
8.5.3 Structural Navigation
8.6 Discovering Insightful Implications
8.6.1 Visualisation of Implications
8.6.2 Our Data Visualisation Approach
8.6.2.1 Attribute Plot
8.6.2.2 Implication Plot
8.6.2.3 Rules Data Table
8.7 Conclusions and Future Work
References
9 Formal Methods in FCA and Big Data
9.1 Introduction
9.2 Context and Concept Lattice Reduction Methods
9.3 Improved Management of Implications
9.4 Minimal Generators to Represent Knowledge
9.5 Probably Approximately Correct Implication Bases
9.6 Summary and Possible Future Trends
References
10 Towards Distributivity in FCA for Phylogenetic Data
10.1 Motivation
10.2 Models: Lattices, Semilattices, Median Algebras and Median Graphs
10.2.1 Lattices and FCA
10.2.2 Distributive Lattices
10.2.3 Median Graphs
10.3 Algorithm to Produce a Distributive -Semilattice
10.4 A Counter-Example for the Existence of a Minimum Distributive -Semilattice
10.5 Discussion and Perspectives
References
11 Triclustering in Big Data Setting
11.1 Introduction
11.2 Prime Object-Attribute-Condition Triclustering
11.3 Triclustering Extensions
11.3.1 Multimodal Clustering
11.3.2 Many-Valued Triclustering
11.4 Implementations
11.4.1 Map-Reduce-Based Multimodal Clustering
11.4.2 Implementation Aspects and Used Technologies
11.4.3 Parallel Many-Valued Triclustering
11.5 Experiments
11.5.1 Datasets
11.5.2 Results
11.6 Experiments with Parallelisation
11.7 Conclusion
References
Index