Advanced Techniques in Knowledge Discovery and Data Mining

  • Nikhil R. Pal
  • Lakhmi Jain

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

Table of contents

  1. Front Matter
    Pages i-xii
  2. Krzysztof J. Cios, Lukasz A. Kurgan
    Pages 1-26
  3. Joydeep Ghosh, Alexander Strehl
    Pages 75-102
  4. José Ramón Cano, Francisco Herrera, Manuel Lozano
    Pages 127-152
  5. Man Leung Wong, Shing Yan Lee, Kwong Sak Leung
    Pages 153-175
  6. Takumi Ichimura, Shinichi Oeda, Machi Suka, Akira Hara, Kenneth J. Mackin, Katsumi Yoshida
    Pages 177-210
  7. Chin-Teng Lin, Her-Chang Pu, Yin-Cheung Lee
    Pages 211-231
  8. Back Matter
    Pages 253-254

About this book

Introduction

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].

Keywords

Bayesian network Internet algorithms architecture calculus classification clustering data mining database databases evolution evolutionary algorithm knowledge discovery networks visualization

Editors and affiliations

  • Nikhil R. Pal
    • 1
  • Lakhmi Jain
    • 2
  1. 1.Electronics and Communication Sciences UnitIndian Statistical InstituteIndia
  2. 2.KES CenterUniversity of South AustraliaAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/1-84628-183-0
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-85233-867-1
  • Online ISBN 978-1-84628-183-9
  • About this book