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One of the first books on multi-database data mining.
Discusses the various issues regarding the systematic and efficient development of multi-database mining applications.
Multi-database mining is recognized as an important and strategic area of research in data mining. The authors discuss the essential issues relating to the systematic and efficient development of multi-database mining applications, and present approaches to the development of data warehouses at different branches, demonstrating how carefully selected multi-database mining techniques contribute to successful real-world applications.
In showing and quantifying how the efficiency of a multi-database mining application can be improved by processing more patterns, the book also covers other essential design aspects. These are carefully investigated and include a determination of an appropriate multi-database mining model, how to select relevant databases, choosing an appropriate pattern synthesizing technique, representing pattern space, and constructing an efficient algorithm. The authors illustrate each of these development issues either in the context of a specific problem at hand, or via some general settings.
Developing Multi-Database Mining Applications will be welcomed by practitioners, researchers and students working in the area of data mining and knowledge discovery.
Content Level »Research
Keywords »Clustering - Coding patterns - Exception association rule - Grouping - Heavy association rule - High-frequent association rule - Local pattern analysis - Synthesis of patterns - data mining
Chapter 1: Introduction
1.2 Distributed Data Mining
1.3 Existing Multi-database Mining Approaches
1.4 Applications of Multi-database Mining
1.5 Improving Multi-database Mining
1.6 Future Directions
Chapter 2: An Extended Model of Local Pattern Analysis
2.2 Some Extreme Types of Association Rules in Multiple Databases
2.3 An Extended Model of Local Pattern Analysis for Synthesizing Global Patterns from Local Patterns in Different Databases
2.4 An Application: Synthesizing Heavy Association Rules in Multiple Real Databases
Chapter 3: Mining Multiple Large Databases
3.2. Multi-database Mining Using Local Pattern Analysis
3.3. Generalized Multi-database Mining Techniques
3.4. Specialized Multi-database Mining Techniques
3.5. Mining Multiple Databases Using Pipelined Feedback Model (PFM)
3.6. Error Evaluation
Chapter 4: Mining Patterns of Select Items in Multiple Databases
4.2 Mining Global Patterns of Select Items
4.3 Overall Association Between Two Items in a Database
4.4 An Application: Study of Select Items in Multiple Databases by Grouping
4.5 Related work
Chapter 5: Enhancing Quality of Knowledge Synthesized from Multi-database Mining
5.2 Related work
5.3. Simple Bit Vector (SBV) Coding
5.4 Antecedent-consequent Pair (ACP) Coding
Chapter 6: Efficient Clustering of Databases Induced by Local Patterns
6.2 Problem Statement
6.3 Related Work
6.4 Clustering Databases
Chapter 7: A Framework for Developing Effective Multi-database Mining Applications
7.2 Shortcomings of Existing Approaches to Multi-database Mining
7.3 Improving Multi-database Mining Applications