Similarities in Fuzzy Data Mining From a Cognitive View to Real World Applications 1st editon by Bernadette Bouchon Meunier, Maria Rifqi, Marie Jeanne Lesot ISBN 3540688587 9783540688587

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ISBN 10:       3540688587
ISBN 13: 978-3540688587
Author:         Bernadette Bouchon-Meunier, Maria Rifqi, Marie-Jeanne Lesot

Similarity is a key concept for all attempts to construct human-like automated systems or assistants to human task solving since they are very natural in the human process of categorization, underlying many natural capabilities such as language understanding, pattern recognition or decision-making. In this paper, we study the use of similarities in data mining, basing our discourse on cognitive approaches of similarity stemming for instance from Tversky’s and Rosch’s seminal works, among others. We point out a general framework for measures of comparison compatible with these cognitive foundations, and we show that measures of similarity can be involved in all steps of the data mining process. We then focus on fuzzy logic that provides interesting tools for data mining mainly because of its ability to represent imperfect information, which is of crucial importance when databases are complex, large, and contain heterogeneous, imprecise, vague, uncertain or incomplete data. We eventually illustrate our discourse by examples of similarities used in real-world data mining problems.

Similarities in Fuzzy Data Mining: From a Cognitive View to Real-World Applications 1st Table of contents:

Chapter 1: Introduction
1.1 Overview of Fuzzy Data Mining
1.2 Cognitive View of Fuzzy Systems
1.3 The Need for Similarity in Fuzzy Data Mining
1.4 Key Concepts and Definitions
1.5 Importance and Applications of Fuzzy Data Mining
1.6 Structure of the Book

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Chapter 2: Fundamentals of Fuzzy Logic and Data Mining
2.1 Introduction to Fuzzy Logic
2.2 Basic Principles of Fuzzy Set Theory
2.3 Data Mining: Concepts and Techniques
2.4 Fuzzy Data Mining Techniques
2.5 Role of Similarity in Fuzzy Data Mining
2.6 Challenges in Fuzzy Data Mining

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Chapter 3: Cognitive Approaches to Fuzzy Data Mining
3.1 Cognitive Science and Data Mining
3.2 Cognitive Models and Fuzzy Systems
3.3 Human-Like Decision Making in Fuzzy Systems
3.4 Cognitive Perspective on Similarity Measures
3.5 Applications of Cognitive Approaches in Fuzzy Data Mining

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Chapter 4: Similarity Measures in Fuzzy Data Mining
4.1 Introduction to Similarity Measures
4.2 Classical Similarity Measures
4.3 Fuzzy Similarity Measures
4.4 Cognitive-Inspired Similarity Measures
4.5 Challenges and Limitations of Similarity Measures

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Chapter 5: Algorithms for Fuzzy Data Mining
5.1 Fuzzy Clustering Algorithms
5.2 Fuzzy Classification Techniques
5.3 Association Rule Mining with Fuzzy Data
5.4 Fuzzy Regression and Prediction Models
5.5 Evaluation Metrics for Fuzzy Algorithms

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Chapter 6: Real-World Applications of Fuzzy Data Mining
6.1 Applications in Healthcare and Medicine
6.2 Fuzzy Data Mining in Financial Systems
6.3 Fuzzy Systems for Industrial Process Control
6.4 Applications in Social Media and Web Mining
6.5 Environmental Monitoring and Prediction Using Fuzzy Data Mining

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Chapter 7: Advanced Topics and Emerging Trends
7.1 Fuzzy Data Mining in Big Data Contexts
7.2 Deep Learning and Fuzzy Systems
7.3 Hybrid Fuzzy Approaches in Data Mining
7.4 Evolutionary and Swarm Intelligence in Fuzzy Data Mining
7.5 Ethical Considerations and Future Directions

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Chapter 8: Conclusion
8.1 Summary of Key Concepts and Techniques
8.2 Impact of Cognitive Approaches on Fuzzy Data Mining
8.3 Future Prospects and Challenges in the Field
8.4 Final Thoughts and Directions for Research

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