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Rough Sets

Theoretical Aspects of Reasoning about Data

  • Book
  • © 1991

Overview

Authors:
  1. Zdzisław Pawlak

    1. Institute of Computer Science, Warsaw University of Technology, Poland

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Part of the book series: Theory and Decision Library D: (TDLD, volume 9)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Theoretical Foundations

    1. Knowledge

      • ZdzisĹ‚aw Pawlak
      Pages 1-8

    2. Imprecise Categories, Approximations and Rough Sets

      • ZdzisĹ‚aw Pawlak
      Pages 9-32

    3. Reduction of Knowledge

      • ZdzisĹ‚aw Pawlak
      Pages 33-44

    4. Dependencies in Knowledge Base

      • ZdzisĹ‚aw Pawlak
      Pages 45-50

    5. Knowledge Representation

      • ZdzisĹ‚aw Pawlak
      Pages 51-67

    6. Decision Tables

      • ZdzisĹ‚aw Pawlak
      Pages 68-80

    7. Reasoning about Knowledge

      • ZdzisĹ‚aw Pawlak
      Pages 81-115

  3. Applications

    1. Decision Making

      • ZdzisĹ‚aw Pawlak
      Pages 116-132

    2. Data Analysis

      • ZdzisĹ‚aw Pawlak
      Pages 133-163

    3. Dissimilarity Analysis

      • ZdzisĹ‚aw Pawlak
      Pages 164-187

    4. Switching Circuits

      • ZdzisĹ‚aw Pawlak
      Pages 188-204

    5. Machine Learning

      • ZdzisĹ‚aw Pawlak
      Pages 205-224

  4. Back Matter

    Pages 225-231
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Keywords

About this book

To-date computers are supposed to store and exploit knowledge. At least that is one of the aims of research fields such as Artificial Intelligence and Information Systems. However, the problem is to understand what knowledge means, to find ways of representing knowledge, and to specify automated machineries that can extract useful information from stored knowledge. Knowledge is something people have in their mind, and which they can express through natural language. Knowl­ edge is acquired not only from books, but also from observations made during experiments; in other words, from data. Changing data into knowledge is not a straightforward task. A set of data is generally disorganized, contains useless details, although it can be incomplete. Knowledge is just the opposite: organized (e.g. laying bare dependencies, or classifications), but expressed by means of a poorer language, i.e. pervaded by imprecision or even vagueness, and assuming a level of granularity. One may say that knowledge is summarized and organized data - at least the kind of knowledge that computers can store.

Authors and Affiliations

  • Institute of Computer Science, Warsaw University of Technology, Poland

    Zdzisław Pawlak

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