Skip to main content
SpringerLink
Log in
Book cover

Advanced Techniques in Knowledge Discovery and Data Mining

  • Book
  • © 2005

Overview

Editors:
  1. Nikhil R. Pal

    1. Electronics and Communication Sciences Unit, Indian Statistical Institute, India

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Lakhmi Jain

    1. KES Center, University of South Australia, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Clear and concise explanations to understand the learning paradigms
  • Chapters written by leading world experts
  • A highly practical book

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Trends in Data Mining and Knowledge Discovery

    • Krzysztof J. Cios, Lukasz A. Kurgan
    Pages 1-26

  3. Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data

    • Andreas König, Achim Gratz
    Pages 27-74

  4. Clustering and Visualization of Retail Market Baskets

    • Joydeep Ghosh, Alexander Strehl
    Pages 75-102

  5. Segmentation of Continuous Data Streams Based on a Change Detection Methodology

    • Gil Zeira, Mark Last, Oded Maimon
    Pages 103-126

  6. Instance Selection Using Evolutionary Algorithms: An Experimental Study

    • José Ramón Cano, Francisco Herrera, Manuel Lozano
    Pages 127-152

  7. Using Cooperative Coevolution for Data Mining of Bayesian Networks

    • Man Leung Wong, Shing Yan Lee, Kwong Sak Leung
    Pages 153-175

  8. Knowledge Discovery and Data Mining in Medicine

    • Takumi Ichimura, Shinichi Oeda, Machi Suka, Akira Hara, Kenneth J. Mackin, Katsumi Yoshida
    Pages 177-210

  9. Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network

    • Chin-Teng Lin, Her-Chang Pu, Yin-Cheung Lee
    Pages 211-231

  10. Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets

    • Shusaku Tsumoto
    Pages 233-252

  11. Back Matter

    Pages 253-254
    Download chapter PDF
Back to top

Keywords

About this book

Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis [2].

Reviews

From the reviews:

"This book presents research on some of the recent advances in the field of DMKD, and provides a glimpse into some real-world applications. … book starts with a preface by the editors, including background information as well as an overview of the book’s contents. … The overall layout and the length of the volume … appear to be satisfactory. … postgraduate students and the faculty members in the business intelligence or DMKD fields would find this volume to be a useful addition to their libraries." (C. S. Arora, Computing Reviews, April, 2006)

Editors and Affiliations

  • Electronics and Communication Sciences Unit, Indian Statistical Institute, India

    Nikhil R. Pal

  • KES Center, University of South Australia, Australia

    Lakhmi Jain

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation