Developing Multi-Database Mining Applications - Softcover
Book 24 of 66: Advanced Information and Knowledge Processing
Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Animesh Adhikari is an associate professor in the department of Computer Science, Chowgule College, Goa, India. His education includes: Doctor of Philosophy in Computer Science, Goa University, Goa, India (2009); Master of Technology in Computer Science, Indian Statistical Institute, Kolkata, India (1993); Master of Computer Application, Jadavpur University, Kolkata, India (1991). The dissertations he has written cover: [Ph D] Knowledge Discovery in Databases with an Emphasis on Multiple Large Databases (Goa University, 2009). This dissertation has the following parts: (i) Association analysis and patterns recognition in a database, (ii) Pattern recognition in multiple databases, (iii) Developing better multi-database mining applications; [M Tech] Fractal-based Image Segmentation (Indian Statistical Institute, 1993). Adhikari's areas of interest include: data mining and knowledge discovery, database systems, decision support systems, artificial intelligence, statistics and other related topics. Adhikari's professional activities are: Member, Program Committee, Indian International Conference on Artificial Intelligence (2009) Session Chair, Data Mining and Knowledge Discovery, Indian International Conference on Artificial Intelligence (2009); Reviewer, IEEE Transactions on Knowledge and Data Engineering journal; Member, Editorial Board, International Journal of Knowledge-Based Organizations, IGI Global (2009 - date); Member, Program Committee, Ph D Workshop; International Conference on Management of Data (2009); Reviewer, IEEE Transactions on Parallel and Distibuted Systems journal. Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. Dr. Pedrycz is an IEEE Fellow, IFSA Fellow and a Fellow of the Engineering Institute of Canada (EIC). Dr. Pedrycz received the M.Sc, and Ph.D, D.Sci
From the Back Cover
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.
"About this title" may belong to another edition of this title.
