Rough computational methods for information systems
J. W. Guan,David A. Bell +1 more
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TLDR
Computational methods for the rough analysis of databases, a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty, are discussed.About:
This article is published in Artificial Intelligence.The article was published on 1998-10-01 and is currently open access. It has received 178 citations till now. The article focuses on the topics: Rough set & Knowledge representation and reasoning.read more
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MGRS: A multi-granulation rough set
TL;DR: It is shown that some of the properties of Pawlak's rough set theory are special instances of those of MGRS, and several important measures are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure.
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Positive approximation: An accelerator for attribute reduction in rough set theory
TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.
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Review: Dimensionality reduction based on rough set theory: A review
K. Thangavel,A. Pethalakshmi +1 more
TL;DR: The rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have been reviewed and the performance analysis of the algorithms has been discussed in connection with the classification.
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Incomplete Multigranulation Rough Set
TL;DR: Several elementary measures are proposed for this rough-set framework, and a concept of approximation reduct is introduced to characterize the smallest attribute subset that preserves the lower approximation and upper approximation of all decision classes in this rough set model.
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Discernibility matrix simplification for constructing attribute reducts
TL;DR: This paper proposes a reduct construction method based on discernibility matrix simplification, which works in a similar way to the classical Gaussian elimination method for solving a system of linear equations.
References
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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.
Book
Knowledge Discovery in Databases
Gregory Piateski,William Frawley +1 more
TL;DR: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases, which spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.
Book
Principles of Database Systems
TL;DR: A large part is a description of relations, their algebra and calculus, and the query languages that have been designed using these concepts and explanations of how the theory can be used to design good systems.
Book
Rough Sets and Data Mining: Analysis of Imprecise Data
Tsau Young Lin,Nick Cercone +1 more
TL;DR: Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information.