LNAI 2682 Integrity Constraints over Association Rules 1st Edition by Artur Bykowski, Thomas Daurel, Nicolas Méger, Christophe Rigotti ISBN 9783540224792 354022479X

In this paper, we propose to investigate the notion of integrity constraints in inductive databases. We advocate that integrity constraints can be used in this context as an abstract concept to encompass common data mining tasks such as the detection of corrupted data or of patterns that contradict the expert beliefs. To illustrate this possibility we propose a form of constraints called association map constraints to specify authorized confidence variations among the association rules. These constraints are easy to read and thus can be used to write clear specifications. We also present experiments showing that their satisfaction can be tested in practice.

LNAI 2682 Integrity Constraints over Association Rules 1st Edition Table of contents:

Chapter 1: Introduction to Association Rule Mining

• What Are Association Rules?
• Key Concepts: Support, Confidence, Lift
• The Role of Association Rules in Data Mining
• Algorithms for Mining Association Rules (e.g., Apriori, FP-growth)
• Applications of Association Rule Mining

Chapter 2: Understanding Integrity Constraints

• What Are Integrity Constraints?
• Types of Integrity Constraints: Domain, Referential, and Semantic Constraints
• Integrity Constraints in the Context of Data Mining
• The Importance of Integrity Constraints for Reliable Data Analysis

Chapter 3: Integrity Constraints in Association Rule Mining

• Challenges in Rule Mining with Constraints
• Integrating Integrity Constraints into Association Rule Mining
• Types of Constraints: Hard Constraints vs. Soft Constraints
• Case Studies: The Role of Integrity Constraints in Real-World Rule Mining

Chapter 4: Rule Quality and Evaluation with Integrity Constraints

• Evaluating Association Rule Quality
• Constraint-Based Filtering and Pruning of Rules
• Metrics for Evaluating Constraint-Aware Rules
• Practical Examples of Evaluating Rules with Integrity Constraints

Chapter 5: Declarative Methods for Integrating Constraints

• Using Declarative Languages for Specifying Integrity Constraints
• Expressing Constraints on Itemsets, Rules, and Data
• Optimizing Rule Mining Queries with Integrity Constraints
• Example: SQL-Based Approaches for Integrity Constraints

Chapter 6: Algorithms for Mining Association Rules with Integrity Constraints

• Modifying Classic Algorithms (Apriori, FP-growth) to Support Constraints
• Constraint-Specific Mining Algorithms
• Handling Soft and Dynamic Constraints
• Performance Optimization for Constraint-Aware Rule Mining

Chapter 7: Case Studies in Constraint-Aware Association Rule Mining

• Case Study 1: Retail Data Analysis and Market Basket Rules
• Case Study 2: Healthcare Data and Constraint-Aware Decision Rules
• Case Study 3: Fraud Detection and Integrity Constraints in Financial Data
• Lessons Learned from Real-World Applications

Chapter 8: Extending Association Rule Mining with Advanced Constraints

• Temporal and Spatial Constraints in Rule Mining
• Incorporating User Preferences as Constraints
• Multi-Dimensional Constraints in Complex Data Mining
• Advanced Techniques for Integrating Business Rules

Chapter 9: Future Directions and Open Challenges

• Emerging Trends in Integrity Constraints for Data Mining
• Integrating Integrity Constraints with Other Data Mining Paradigms (e.g., Classification, Clustering)
• Research Opportunities and Open Problems
• The Future of Constraint-Based Rule Mining in Big Data

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