Course Summary
Formal Concept Analysis is a mathematical method for conceptual knowledge representation. This course will teach you how to use Formal Concept Analysis to build conceptual models, understand data, and solve problems.Key Learning Points
- Learn how to use Formal Concept Analysis to build conceptual models and understand data
- Understand the mathematical foundations of Formal Concept Analysis
- Apply Formal Concept Analysis to real-world problems
Job Positions & Salaries of people who have taken this course might have
- Data Scientist
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- Business Analyst
- USA: $85,000
- India: ₹900,000
- Spain: €35,000
Related Topics for further study
- Mathematical Modeling
- Conceptual Knowledge Representation
- Data Analysis
- Problem Solving
- Machine Learning
Learning Outcomes
- Understand the mathematical foundations of Formal Concept Analysis
- Build conceptual models using Formal Concept Analysis
- Apply Formal Concept Analysis to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of mathematics
- Familiarity with data analysis
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Introduction to Formal Concept Analysis
- Data Analysis and Visualization with R
Related Education Paths
Related Books
Description
This course is an introduction into formal concept analysis (FCA), a mathematical theory oriented at applications in knowledge representation, knowledge acquisition, data analysis and visualization. It provides tools for understanding the data by representing it as a hierarchy of concepts or, more exactly, a concept lattice. FCA can help in processing a wide class of data types providing a framework in which various data analysis and knowledge acquisition techniques can be formulated. In this course, we focus on some of these techniques, as well as cover the theoretical foundations and algorithmic issues of FCA.
Outline
- Formal concept analysis in a nutshell
- About the University
- Welcome to Formal Concept Analysis
- What is formal concept analysis?
- Understanding the concept lattice diagram
- Reading concepts from the lattice diagram
- Reading implications from the lattice diagram
- Conceptual clustering
- Formal contexts and derivation operators
- Formal concepts
- Closure operators
- Closure systems
- Software: Concept Explorer
- Many-valued contexts
- Conceptual scaling schemas
- Scaling ordinal data
- About University
- Rules on the academic integrity in the course
- Further reading
- First-week slides
- Reading concept lattice diagrams
- Formal concepts and closure operators
- Concept lattices and their line diagrams
- The partial order on concepts
- Supremum and infimum
- Lattices
- The basic theorem (I)
- The basic theorem (II)
- Line diagrams
- Context clarification and reduction
- Context reduction: an example
- Supremum and infimum
- Lattices and complete lattices
- Clarification and reduction
- Constructing concept lattices
- Finding the concepts
- Drawing a concept lattice diagram
- A naive algorithm for enumerating closed sets
- Representing sets by bit vectors
- Closures in lectic order
- Next Closure through an example
- The complexity of the algorithm
- Basic incremental strategy
- An example
- The definition of implications
- Examples of attribute implications
- Implication inference
- Computing the closure under implications
- Third-week slides
- Transposed context
- Closures in lectic order
- Implications
- Implications
- Redundancy in implications
- Pseudo-closed sets and canonical basis
- Preclosed sets
- Preclosure operator
- Computing the canonical basis
- An example
- Complexity issues
- Functional dependencies
- Translation between functional dependencies and implications
- Fourth-week slides
- Implications and pseudo-intents
- Canonical basis
- Functional dependencies
- Interactive algorithms for learning implications
- Basic introduction to learning with queries
- Learning binary patterns
- An easy case
- The general case
- Learning implications with queries
- Membership and equivalence queries for implications
- A polynomial-time algorithm
- Learning domain implications with queries
- Attribute exploration algorithm
- Attribute exploration of pairs of squares
- Object exploration
- Variations of attribute exploration
- Incompletely specified examples
- Completing incomplete contexts
- Fifth-week slides
- Learning with queries
- Learning implications with membership and equivalence queries
- Attribute exploration
- Working with real data
- Small changes in the context, big changes in the concept lattice
- Iceberg lattices
- Concept stability
- Separation index
- Concept probability
- Nested line diagrams
- Association rules
- Support and confidence
- Frequent closed sets
- Luxenburger basis
- Goodbye!
- Sixth-week slides
- Concept indices
- Association rules
Summary of User Reviews
The Formal Concept Analysis course on Coursera is highly recommended by users for its in-depth coverage of the subject matter and practical applications. Many users found the course to be engaging and well-structured, making it easy to follow along and retain the information.Key Aspect Users Liked About This Course
The course is highly informative and provides a thorough understanding of Formal Concept Analysis.Pros from User Reviews
- Well-structured and engaging content
- In-depth coverage of the subject matter
- Practical applications of Formal Concept Analysis
- Experienced and knowledgeable instructors
- Useful exercises and assignments
Cons from User Reviews
- Some users found the course to be too technical
- Limited interaction with instructors and peers
- Not suitable for beginners
- Requires prior knowledge of mathematics and computer science
- Some users reported technical issues with the platform
Sergei Obiedkov
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