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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

  • Book
  • © 2006

Overview

Editors:
  1. Evangelos Triantaphyllou

    1. Louisiana State University, Baton Rouge, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Giovanni Felici

    1. Consiglio Nazionale delle Ricerche, Rome, Italy

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    You can also search for this editor in PubMed   Google Scholar

  • Provides a unique perspective into the core of data mining and knowledge discovery (DM and KD), combining many theoretical foundations for the behavior and capabilities of various DM and KD methods
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xlviii
    Download chapter PDF

  2. A Common Logic Approach to Data Mining and Pattern Recognition

    • Arkadij D. Zakrevskij
    Pages 1-43

  3. The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery

    • Evangelos Triantaphyllou
    Pages 45-87

  4. An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process

    • Xenia Naidenova
    Pages 89-147

  5. Discovering Rules That Govern Monotone Phenomena

    • Vetle I. Torvik, Evangelos Triantaphyllou
    Pages 149-192

  6. Learning Logic Formulas and Related Error Distributions

    • Giovanni Felici, Fushing Sun, Klaus Truemper
    Pages 193-226

  7. Feature Selection for Data Mining

    • Vanda de Angelis, Giovanni Felici, Gabriella Mancinelli
    Pages 227-252

  8. Transformation of Rational Data and Set Data to Logic Data

    • Stephen Bartnikowski, Matthias Granberry, Jonathan Mugan, Klaus Truemper
    Pages 253-278

  9. Data Farming: Concepts and Methods

    • Andrew Kusiak
    Pages 279-304

  10. Rule Induction Through Discrete Support Vector Decision Trees

    • Carlotta Orsenigo, Carlo Vercellis
    Pages 305-326

  11. Multi-Attribute Decision Trees and Decision Rules

    • Jun-Youl Lee, Sigurdur Olafsson
    Pages 327-358

  12. Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective

    • Lian-Yin Zhai, Li-Pheng Khoo, Sai-Cheong Fok
    Pages 359-394

  13. Discovering Knowledge Nuggets with a Genetic Algorithm

    • Edgar Noda, Alex A. Freitas
    Pages 395-432

  14. Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems

    • Michael Kirley, Hussein A. Abbass, Robert (Bob) I. McKay
    Pages 433-457

  15. Fuzzy Logic in Discovering Association Rules: An Overview

    • Guoqing Chen, Qiang Wei, Etienne E. Kerre
    Pages 459-493

  16. Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview

    • T. Warren Liao
    Pages 495-550

  17. Data Mining from Multimedia Patient Records

    • Adel S. Elmaghraby, Mehmed M. Kantardzic, Mark P. Wachowiak
    Pages 551-595

  18. Learning to Find Context Based Spelling Errors

    • Hisham Al-Mubaid, Klaus Truemper
    Pages 597-627

  19. Induction and Inference with Fuzzy Rules for Textual Information Retrieval

    • Jianhua Chen, Donald H. Kraft, Maria J. Martin-Bautista, Maria-Amparo Vila
    Pages 629-653

  20. Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem

    • Dean H. Judson
    Pages 655-694
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Keywords

About this book

2. Some Background Information 49 3. Definitions and Terminology 52 4. The One Clause at a Time (OCAT) Approach 54 4. 1 Data Binarization 54 4. 2 The One Clause at a Time (OCAT) Concept 58 4. 3 A Branch-and-Bound Approach for Inferring Clauses 59 4. 4 Inference of the Clauses for the Illustrative Example 62 4. 5 A Polynomial Time Heuristic for Inferring Clauses 65 5. A Guided Learning Approach 70 6. The Rejectability Graph of Two Collections of Examples 72 6. 1 The Definition of the Rej ectability Graph 72 6. 2 Properties of the Rejectability Graph 74 6. 3 On the Minimum Clique Cover of the Rej ectability Graph 76 7. Problem Decomposition 77 7. 1 Connected Components 77 7. 2 Clique Cover 78 8. An Example of Using the Rejectability Graph 79 9. Conclusions 82 References 83 Author's Biographical Statement 87 Chapter 3 AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova 89 1. Introduction 90 2. A Model of Rule-Based Logical Inference 96 2. 1 Rules Acquired from Experts or Rules of the First Type 97 2. 2 Structure of the Knowledge Base 98 2. 3 Reasoning Operations for Using Logical Rules of the First Type 100 2. 4 An Example of the Reasoning Process 102 3. Inductive Inference of Implicative Rules From Examples 103 3.

Editors and Affiliations

  • Louisiana State University, Baton Rouge, USA

    Evangelos Triantaphyllou

  • Consiglio Nazionale delle Ricerche, Rome, Italy

    Giovanni Felici

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