Developing Multi-Database Mining Applications / Edition 1
by Animesh Adhikari, Pralhad Ramachandrarao, Witold PedryczISBN-10: 1849960437
ISBN-13: 9781849960434
Pub. Date: 06/08/2010
Publisher: Springer London
Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate
Overview
Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.
Product Details
- ISBN-13:
- 9781849960434
- Publisher:
- Springer London
- Publication date:
- 06/08/2010
- Series:
- Advanced Information and Knowledge Processing Series
- Edition description:
- 2010
- Pages:
- 130
- Product dimensions:
- 6.40(w) x 9.30(h) x 0.60(d)
Table of Contents
Chapter 1: Introduction
1.1 Motivation
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
2.1 Introduction
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
2.5 Conclusions
Chapter 3: Mining Multiple Large Databases
3.1 Introduction
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
3.7. Experiments
3.8. Conclusions
Chapter 4: Mining Patterns of Select Items in Multiple Databases
4.1 Introduction
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
4.6 Conclusions
Chapter 5: Enhancing Quality of Knowledge Synthesized from Multi-database Mining
5.1 Introduction
5.2 Related work
5.3. Simple Bit Vector (SBV) Coding
5.4 Antecedent-consequent Pair (ACP) Coding
5.5 Experiments
5.6 Conclusions
Chapter 6: Efficient Clustering of Databases Induced by Local Patterns
6.1 Introduction
6.2 Problem Statement
6.3 Related Work
6.4 Clustering Databases
6.5 Experiments
6.6 Conclusions
Chapter 7: A Framework for Developing Effective Multi-database Mining Applications
7.1 Introduction
7.2 Shortcomings of Existing Approaches to Multi-database Mining
7.3 Improving Multi-database Mining Applications
7.4 Conclusions
References
Index
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