Developing Multi-Database Mining Applications

  • Animesh Adhikari
  • Pralhad Ramachandrarao
  • Witold Pedrycz

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 1-13
  3. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 15-35
  4. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 37-50
  5. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 51-70
  6. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 71-94
  7. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 95-120
  8. Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz
    Pages 121-127
  9. Back Matter
    Pages 129-129

About this book

Introduction

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.

Keywords

Clustering Coding patterns Exception association rule Grouping Heavy association rule High-frequent association rule Local pattern analysis Synthesis of patterns data mining

Authors and affiliations

  • Animesh Adhikari
    • 1
  • Pralhad Ramachandrarao
    • 2
  • Witold Pedrycz
    • 3
  1. 1.Dept. Computer ScienceSmt.Parvatibal Chowgule CollegeMargoaIndia
  2. 2.Dept. Computer Science & TechnologyGoa UniversityGoaIndia
  3. 3.Dept. Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84996-044-1
  • Copyright Information Springer-Verlag London 2010
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84996-043-4
  • Online ISBN 978-1-84996-044-1
  • Series Print ISSN 1610-3947
  • About this book