Data mining : a heuristic approach /

Bibliographic Details
Other Authors: Abbass, Hussein A., Sarker, Ruhul A., Newton, Charles S.
Format: Printed Book
Published: Hershey : Idea Group, c2002.
Subjects:
Data mining.
Database searching.
Heuristic programming.
Online Access:http://www.loc.gov/catdir/toc/fy031/2001039775.html
Table of Contents:
  • Machine generated contents note: Part One: General Heuristics
  • Chapter 1: From Evolution to Immune to Swarm to?
  • A Simple Introduction to Modern Heuristics1
  • Hussein A. Abbass, University of New South Wales, Australia
  • Chapter 2: Approximating Proximityfor Fast andRobust
  • Distance-Based Clustering22
  • Vladimir Estivill-Castro, University of Newcastle, Australia
  • Michael Houle, University of Sydney, Australia
  • Part Two: Evolutionary Algorithms
  • Chapter3: On the Use of Evolutionary Algorithmsin Data Mining48
  • Erick Cantu-Paz, Lawrence Livermore National Laboratory, USA
  • Chandrika Kamath, Lawrence Livermore National Laboratory, USA
  • Chapter 4: The discovery of interesting nuggets using heuristic techniques72
  • Beatriz de la Iglesia, University of East Anglia, UK
  • Victor J. Rayward-Smith, University of East Anglia, UK
  • Chapter5: Estimation of Distribution Algorithms forFeature Subset
  • Selection in Large Dimensionality Domains97
  • Ifiaki Inza, University of the Basque Country, Spain
  • Pedro Larranaga, University of the Basque Country, Spain
  • Basilio Sierra, University of the Basque Country, Spain
  • Chapter 6: Towards the Cross-Fertilization of Multiple Heuristics:
  • Evolving Teams of Local Bayesian Learners117
  • Jorge Muruzdbal, Universidad Rey Juan Carlos, Spain
  • Chapter 7: Evolution of SpatialData Templates for Object Classification143
  • Neil Dunstan, University of New England, Australia
  • Michael de Raadt, University of Southern Queensland, Australia
  • Part Three: Genetic Programming
  • Chapter 8: Genetic Programming as a Data-Mining Tool157
  • Peter W.H. Smith, City University, UK
  • Chapter 9: A Building BlockApproach to Genetic Programming
  • for Rule Discovery174
  • A.P. Engelbrecht, University of Pretoria, South Africa
  • Sonja Rouwhorst, Vrije Universiteit Amsterdam, The Netherlands
  • L. Schoeman, University of Pretoria, South Africa
  • Part Four: Ant Colony Optimization and Immune Systems
  • Chapter 10: An Ant Colony Algorithm for Classification Rule Discovery 191
  • Rafael S. Parpinelli, Centro Federal de Educacao Tecnologica do Parana, Brazil
  • Heitor S. Lopes, Centro Federal de Educacao Tecnologica do Parana, Brazil
  • Alex A. Freitas, Pontificia Universidade Catolica do Parana, Brazil
  • Chapter 11: ArtificialImmune Systems: Using the Immune System
  • as Inspiration forDataMining209
  • Jon Timmis, University of Kent at Canterbury, UK
  • Thomas Knight, University of Kent at Canterbury, UK
  • Chapter 12: aiNet: An Artificial Immune Network for Data Analysis231
  • Leandro Nunes de Castro, State University of Campinas, Brazil
  • Fernando J. Von Zuben, State University of Campinas, Brazil
  • Part Five: Parallel Data Mining
  • Chapter 13: Parallel Data Mining261
  • David Taniar, Monash University, Australia
  • J. Wenny Rahayu, La Trobe University, Australia.

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