LNAI 2682 Towards a Logic Query Language for Data Mining 1st Edition by Fosca Giannotti, Giuseppe Manco, Franco Turini ISBN 9783540224792 354022479X

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Product details:
ISBN 10: 354022479X
ISBN 13: 9783540224792
Author: Fosca Giannotti, Giuseppe Manco, Franco Turini

We present a logic database language with elementary data mining mechanisms to model the relevant aspects of knowledge discovery, and to provide a support for both the iterative and interactive features of the knowledge discovery process. We adopt the notion of user-defined aggregate to model typical data mining tasks as operations unveiling unseen knowledge. We illustrate the use of aggregates to model specific data mining tasks, such as frequent pattern discovery, classification, data discretization and clustering, and show how the resulting data mining query language allows the modeling of typical steps of the knowledge discovery process, that range from data preparation to knowledge extraction and evaluation.

LNAI 2682 Towards a Logic Query Language for Data Mining 1st Edition Table of contents:

Chapter 1: Introduction to Data Mining and Query Languages

• What is Data Mining?
• Overview of Traditional Query Languages
• The Need for a Logic-Based Approach in Data Mining
• Key Concepts: Querying, Mining, and Logic

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Chapter 2: Foundations of Logic and Formal Languages

• Basics of Logic: Propositional and Predicate Logic
• Formal Languages in Computing
• Logic Programming and Its Application in Data Mining
• The Role of Logic in Representing Knowledge

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Chapter 3: Query Languages for Data Mining

• Traditional SQL-Based Query Languages
• Existing Query Languages in Data Mining: Limitation and Extensions
• Comparison Between Logic-Based and Traditional Query Languages
• Why Logic-Based Query Languages Are Needed

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Chapter 4: Developing a Logic Query Language for Data Mining

• Requirements for a Logic-Based Query Language
• Syntax and Semantics of the Proposed Language
• Designing the Language Structure: Rules, Operators, and Variables
• Case Studies: Implementing a Simple Query in Logic

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Chapter 5: Advanced Features of Logic Query Languages

• Expressing Complex Queries with Logic
• Recursive Queries and Logic in Data Mining
• Handling Uncertainty and Incomplete Data with Logic
• Extending the Language for Multi-Dimensional Data

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Chapter 6: Optimization and Efficiency of Logic Queries

• Query Optimization in Data Mining
• Strategies for Efficient Query Processing in Logic Languages
• Indexing and Caching Techniques for Logic-Based Queries
• Benchmarking and Performance Metrics

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Chapter 7: Applications of Logic-Based Query Languages in Data Mining

• Case Study 1: Market Basket Analysis Using Logic Queries
• Case Study 2: Bioinformatics and Genomic Data Mining
• Case Study 3: Fraud Detection and Anomaly Detection in Databases
• Practical Benefits of Using Logic Query Languages in Real-World Scenarios

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Chapter 8: Challenges and Open Problems

• Scalability of Logic-Based Query Languages
• Handling Large-Scale Data with Logic Queries
• Integration with Existing Data Mining Tools and Frameworks
• Open Problems and Areas for Future Research

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Chapter 9: Towards a Unified Framework for Data Mining

• Integrating Logic-Based Query Languages with Other Data Mining Paradigms
• Logic and Machine Learning Integration
• Future Directions in Data Mining Query Languages
• Building Unified Frameworks for Cross-Domain Data Mining

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