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Evolutionary Computation in Data Mining

  • Book
  • © 2005

Overview

Editors:
  1. Ashish Ghosh

    1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

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  2. Lakhmi C. Jain

    1. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, Adelaide, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • State of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms
  • Demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 163)

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Table of contents (12 chapters)

  1. Front Matter

    Pages I-XVII
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  2. Evolutionary Algorithms for Data Mining and Knowledge Discovery

    • Ashish Ghosh
    Pages 1-20

  3. Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining

    • Jose Ramon Cano, Francisco Herrera, Manuel Lozano
    Pages 21-39

  4. GAP: Constructing and Selecting Features with Evolutionary Computing

    • Matthew G. Smith, Larry Bull
    Pages 41-56

  5. Multi-Agent Data Mining using Evolutionary Computing

    • Riyaz Sikora
    Pages 57-78

  6. A Rule Extraction System with Class-Dependent Features

    • Xiuju Fu, Lipo Wang
    Pages 79-99

  7. Knowledge Discovery in Data Mining via an Evolutionary Algorithm

    • Qi Yu, Kay Chen Tan, Tong Heng Lee
    Pages 101-123

  8. Diversity and Neuro-Ensemble

    • Minh Ha Nguyen, Hussein Abbass, Robert McKay
    Pages 125-156

  9. Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets

    • Olfa Nasraoui, Elizabeth Leon, Raghu Krishnapuram
    Pages 157-188

  10. Evolutionary Computation in Intelligent Network Management

    • Ajith Abraham
    Pages 189-210

  11. Genetic Programming in Data Mining for Drug Discovery

    • W. B. Langdon, S. J. Barrett
    Pages 211-235

  12. Microarray Data Mining with Evolutionary Computation

    • Gary B. Fogel
    Pages 237-248

  13. An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts

    • Po-Chang (P.C.) Ko, Ping-Chen (P.C.) Lin
    Pages 249-263

  14. Back Matter

    Pages 264-265
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Keywords

About this book

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

Editors and Affiliations

  • Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  • Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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