Advanced Methods for Knowledge Discovery from Complex Data

  • Sanghamitra Bandyopadhyay
  • Ujjwal Maulik
  • Lawrence B. Holder
  • Diane J. Cook
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Foundations

    1. Sanghamitra Bandyopadhyay, Ujjwal Maulik
      Pages 3-42
    2. Joydeep Ghosh, Shailesh Kumar, Melba M. Crawford
      Pages 43-73
    3. Diane J. Cook, Lawrence B. Holder, Jeff Coble, Joseph Potts
      Pages 75-93
    4. Thomas Gärtner
      Pages 95-121
    5. Sunita Sarawagi
      Pages 153-187
    6. Lise Getoor
      Pages 189-207
  3. Applications

    1. Sen Zhang, Jason T. L. Wang
      Pages 211-230
    2. Tao Jiang, Ah-Hwee Tan
      Pages 231-252
    3. Sanjoy Kumar Saha, Amit Kumar Das, Bhabatosh Chanda
      Pages 253-283
    4. Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady Zaslavsky
      Pages 307-335
    5. Jiong Yang, Xifeng Yan, Jiawei Han, Wei Wang
      Pages 337-363
  4. Back Matter
    Pages 365-369

About this book

Introduction

Advanced Methods for Knowledge Discovery from Complex Data brings together research articles by active practitioners and leading researchers reporting recent advances in the field of knowledge discovery, where the information is mined from complex data, such as unstructured text from the world-wide web, databases naturally represented as graphs and trees, geoscientific data from satellites and visual images, multimedia data and bioinformatics data.

An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining, including descriptions of some currently popular tools like genetic algorithms, neural networks and case-based reasoning. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks.

With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, as well as recent working applications this book provides a representative selection of the available methods and their evaluation in real domains. It will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field. A website supports the book: http://www.cse.uta.edu/amkdcd.

Keywords

Bayesian network algorithms classification computer science data mining database evolution graph image databases kernel knowledge knowledge discovery learning machine learning ontology

Authors and affiliations

  • Sanghamitra Bandyopadhyay
    • 1
  • Ujjwal Maulik
    • 2
  • Lawrence B. Holder
    • 3
  • Diane J. Cook
    • 3
  1. 1.Indian Statistical InstituteMachine Intelligence UnitKolkataIndia
  2. 2.Department of Computer Science & Engineering, Jadavpur UniversityKolkataIndia
  3. 3.University of Texas at ArlingtonDepartment of Computer Science & EngineeringUSA

Bibliographic information

  • DOI https://doi.org/10.1007/1-84628-284-5
  • Copyright Information Dr Sanghamitra Bandyopadhyay 2005
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-85233-989-0
  • Online ISBN 978-1-84628-284-3
  • About this book