Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Parallel communicating grammar systems with negotiation
Fundamenta Informaticae - Special issue: to the memory of Prof. Helena Rasiowa
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fundamenta Informaticae
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Contributions to the theory of rough sets
Fundamenta Informaticae
Rough sets and association rule generation
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Incomplete Information: Rough Set Analysis
Incomplete Information: Rough Set Analysis
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Generation of Rules from Incomplete Information Systems
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Boolean Reasoning Scheme with Some Applications in Data Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Taming Large Rule Models in Rough Set Approaches
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Efficient SQL-Querying Method for Data Mining in Large Data Bases
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Rough Sets and Knowledge Discovery: An Overview
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Rough Set Approach to the Survival Analysis
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Using optimisation techniques for discretizing rough set partitions
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
Transactions on Rough Sets IX
Research on rough set theory and applications in China
Transactions on rough sets VIII
The Knowledge Engineering Review
Towards a practical approach to discover internal dependencies in rule-based knowledge bases
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Knowledge discovery by relation approximation: a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Knowledge extraction from intelligent electronic devices
Transactions on Rough Sets III
Time complexity of decision trees
Transactions on Rough Sets III
Comparative analysis of deterministic and nondeterministic decision tree complexity local approach
Transactions on Rough Sets IV
Zdzisław pawlak: life and work
Transactions on Rough Sets V
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Reduct and variance based clustering of high dimensional dataset
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Unsupervised feature selection in digital mammogram image using rough set theory
International Journal of Bioinformatics Research and Applications
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Rough set theory was proposed by Zdzislaw Pawlak (1982, 1991) in the early 1980s. Since then we have witnessed a systematic, worldwide growth of interest in rough set theory and its applications. The rough set approach has been introduced to deal with vague or imprecise concepts, to derive knowledge from data, and to reason about knowledge derived from data. In the first part of this chapter we outline the basic notions of rough sets, especially those that are related to knowledge extraction from data. Searching for knowledge is usually guided by some constraints (Langley et al., 1987). A wide class of such constraints can be expressed by discernibility of objects. Knowledge derived from data by the rough set approach consists of different constructs. Among them there are reducts, which are the central construct in the rough set approach, different kinds of rules (such as decision rules or association rules), dependencies, and patterns (templates), or classifiers. The reducts are of special importance since all other constructs can be derived from different kinds of reducts using the rough set approach. Strategies for searching reducts apply Boolean (propositional) reasoning (Brown, 1990), since the constraints (e.g., constraints related to the discernibility of objects) are expressible by propositional formulas. Moreover, using Boolean reasoning, minimal description-length data models (Mitchell, 1997; Rissanen, 1978) can be induced since they correspond to constructs of Boolean functions called prime implicants (or their approximations). The second part of this chapter includes illustrative examples of applications of this general scheme to inducing from data various forms of knowledge.