Rough Sets

1. Front Matter

   Pages i-xvi

   PDF

2. Theoretical Foundations

   1. Knowledge

      • Zdzisław Pawlak

      Pages 1-8

   2. Imprecise Categories, Approximations and Rough Sets

      • Zdzisław Pawlak

      Pages 9-32

   3. Reduction of Knowledge

      • Zdzisław Pawlak

      Pages 33-44

   4. Dependencies in Knowledge Base

      • Zdzisław Pawlak

      Pages 45-50

   5. Knowledge Representation

      • Zdzisław Pawlak

      Pages 51-67

   6. Decision Tables

      • Zdzisław Pawlak

      Pages 68-80

   7. Reasoning about Knowledge

      • Zdzisław Pawlak

      Pages 81-115

3. Applications

   1. Decision Making

      • Zdzisław Pawlak

      Pages 116-132

   2. Data Analysis

      • Zdzisław Pawlak

      Pages 133-163

   3. Dissimilarity Analysis

      • Zdzisław Pawlak

      Pages 164-187

   4. Switching Circuits

      • Zdzisław Pawlak

      Pages 188-204

   5. Machine Learning

      • Zdzisław Pawlak

      Pages 205-224

4. Back Matter

   Pages 225-231

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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.

• Equivalence
• Pattern Recognition
• algorithms
• calculus
• classification
• cognition
• control
• control algorithm
• knowledge
• knowledge base
• knowledge representation
• learning
• machine learning
• semantics

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

  Zdzisław Pawlak

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