LNAI 2682 Declarative Data Mining Using SQL3 1st Edition by Hasan Jamil ISBN 9783540224792 354022479X

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Product details:
ISBN 10: 354022479X
ISBN 13: 9783540224792
Author: Hasan Jamil

Researchers convincingly argue that the ability to declaratively mine and analyze relational databases using SQL for decision support is a critical requirement for the success of the acclaimed data mining technology. Although there have been several encouraging attempts at developing methods for data mining using SQL, simplicity and efficiency still remain significant impediments for further development. In this article, we propose a significantly new approach and show that any object relational database can be mined for association rules without any restructuring or preprocessing using only basic SQL3 constructs and functions, and hence no additional machineries are necessary. In particular, we show that the cost of computing association rules for a given database does not depend on support and confidence thresholds. More precisely, the set of large items can be computed using one simple join query and an aggregation once the set of all possible meets (least fixpoint) of item set patterns in the input table is known. We believe that this is an encouraging discovery especially compared to the well known SQL based methods in the literature. Finally, we capture the functionality of our proposed mining method in a mine by SQL3 operator for general use in any relational database.

LNAI 2682 Declarative Data Mining Using SQL3 1st Edition Table of contents:

Chapter 1: Introduction to Data Mining

• What is Data Mining?
• Key Concepts in Data Mining
• Types of Data Mining Tasks: Classification, Clustering, Association
• Challenges in Data Mining

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Chapter 2: Introduction to SQL3

• Overview of SQL3: The Evolution of SQL
• Features and Enhancements in SQL3
• Declarative Querying in SQL3
• Differences Between SQL2 and SQL3

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Chapter 3: Fundamentals of Declarative Data Mining

• What is Declarative Data Mining?
• The Role of Declarative Queries in Data Mining
• Expressing Data Mining Tasks Using Declarative Statements
• Benefits of Declarative Data Mining Approaches

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Chapter 4: Using SQL3 for Data Mining

• Basic SQL3 Queries for Data Mining
• Complex Querying Techniques with SQL3
• Advanced SQL3 Features for Data Mining: Recursive Queries, Subqueries
• Example Queries in SQL3 for Data Mining Tasks

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Chapter 5: Data Mining with SQL3: Classification

• Overview of Classification in Data Mining
• Implementing Classification Tasks Using SQL3
• Example: SQL3 Queries for Decision Trees and Rule-Based Classifiers
• Evaluating Classification Performance in SQL3

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Chapter 6: Data Mining with SQL3: Clustering

• Overview of Clustering Techniques
• Using SQL3 for Clustering Data
• Example: SQL3 Queries for K-Means and Hierarchical Clustering
• Evaluating Clustering Results with SQL3

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Chapter 7: Data Mining with SQL3: Association Rule Mining

• Understanding Association Rule Mining
• Implementing Association Rules with SQL3
• Example: SQL3 Queries for Apriori Algorithm
• Performance Considerations in SQL3 for Association Mining

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Chapter 8: Complex Data Mining Queries with SQL3

• Combining Multiple Data Mining Tasks in SQL3
• Multi-Step Data Mining Processes Using SQL3
• Optimization of Complex Queries for Large Datasets
• Case Study: Implementing Complex Data Mining in SQL3

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Chapter 9: SQL3 and Big Data Mining

• Challenges of Mining Big Data with SQL3
• Using SQL3 for Mining Large-Scale Datasets
• Integration of SQL3 with Big Data Technologies
• Performance Optimizations for Big Data Mining Queries

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Chapter 10: Future Directions and Research in Declarative Data Mining

• Emerging Trends in Declarative Data Mining
• The Future of SQL in Data Mining
• Integration with Machine Learning and AI
• Open Research Areas in Declarative Data Mining

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