Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

  • Evangelos Triantaphyllou
  • Giovanni Felici

Part of the Massive Computing book series (MACO, volume 6)

Table of contents

  1. Front Matter
    Pages i-xlviii
  2. Vetle I. Torvik, Evangelos Triantaphyllou
    Pages 149-192
  3. Giovanni Felici, Fushing Sun, Klaus Truemper
    Pages 193-226
  4. Vanda de Angelis, Giovanni Felici, Gabriella Mancinelli
    Pages 227-252
  5. Stephen Bartnikowski, Matthias Granberry, Jonathan Mugan, Klaus Truemper
    Pages 253-278
  6. Andrew Kusiak
    Pages 279-304
  7. Carlotta Orsenigo, Carlo Vercellis
    Pages 305-326
  8. Jun-Youl Lee, Sigurdur Olafsson
    Pages 327-358
  9. Lian-Yin Zhai, Li-Pheng Khoo, Sai-Cheong Fok
    Pages 359-394
  10. Edgar Noda, Alex A. Freitas
    Pages 395-432
  11. Michael Kirley, Hussein A. Abbass, Robert (Bob) I. McKay
    Pages 433-457
  12. Guoqing Chen, Qiang Wei, Etienne E. Kerre
    Pages 459-493
  13. Adel S. Elmaghraby, Mehmed M. Kantardzic, Mark P. Wachowiak
    Pages 551-595
  14. Hisham Al-Mubaid, Klaus Truemper
    Pages 597-627
  15. Jianhua Chen, Donald H. Kraft, Maria J. Martin-Bautista, Maria-Amparo Vila
    Pages 629-653
  16. Xiaoting Wang, Peng Zhu, Giovanni Felici, Evangelos Triantaphyllou
    Pages 695-716
  17. Back Matter
    Pages 717-748

About this book


This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples—many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.

The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.


The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.


BAYES Data mining Knowledge discovery LA Pattern Recognition Rule induction STATISTICA

Editors and affiliations

  • Evangelos Triantaphyllou
    • 1
  • Giovanni Felici
    • 2
  1. 1.Louisiana State UniversityBaton RougeUSA
  2. 2.Consiglio Nazionale delle RicercheRomeItaly

Bibliographic information