Open Access
Knowledge discovery by application of rough set models
Jarosław Stepaniuk
- pp 3-136
TLDR
This Chapter discusses selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (prediction) problem.Abstract:
The amount of electronic data available is growing very fast and this explosive growth in databases has generated a need for new techniques and tools that can intelligently and automatically extract implicit, previously unknown, hidden and potentially useful information and knowledge from these data. These tools and techniques are the subject of the field of Knowledge Discovery in Databases. In this Chapter we discuss selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (prediction) problem.read more
Citations
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Journal ArticleDOI
Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
Journal ArticleDOI
Rough sets and Boolean reasoning
Zdzisław Pawlak,Andrzej Skowron +1 more
TL;DR: Methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis are discussed.
On some issues on rough sets
TL;DR: In this article, the authors give rudiments of rough set theory and present some recent research directions proposed by the author, and give a review of some of the most relevant work.
Journal ArticleDOI
Information granules: Towards foundations of granular computing,
TL;DR: This work discusses a problem of synthesis of robust terms, i.e., descriptions of information granules, satisfying a given specification, an important problem for granular computing and its applications for spatial reasoning or knowledge discovery and data mining.
Book ChapterDOI
Rough Set Based Decision Support
TL;DR: The goal of the chapter is to present a knowledge discovery paradigm for multi-attribute and multicriteria decision making, which is based upon the concept of rough sets, in order to find concise classification patterns that agree with situations that are described by the data.
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Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.