Evolutionary Computation in Data Mining

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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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).

• Data mining
• Evolutionary Computation
• Knowledge Discovery in Databases
• Multi-Agent Data mining
• algorithm
• algorithms
• bioinformatics
• databases
• evolutionary algorithm
• genetic programming
• knowledge discovery
• programming

• 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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