2008
DOI: 10.2495/data080011
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An approach to finding reduced sets of information features describing discrete objects based on rough sets theory

Abstract: Modern Data Mining methods allow discovering non-trivial dependencies in large information arrays. Since these methods are used for processing and analysis of huge information volumes, reducing the number of features necessary for describing a discrete object is one of the most important problems.One of the classical problems in intelligent data analysis is the problem of classifying new objects based on some a-priori information. This information might not allow us to exactly classify an object as one belongi… Show more

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“…in our series of papers [2][3][4][5] we try to give the rough set ideology a bit different perspective by avoiding the usage of the knowledge granularity concept. Whereas the classical rough set theory is based on the indiscernibility relation (which can be equivalence, tolerance or other types of relations), we do not use such a notion and try to consider uncertainty from a logic-algebraic viewpoint.…”
Section: Algebraic Approach Versus Knowledge Granularitymentioning
confidence: 99%
“…in our series of papers [2][3][4][5] we try to give the rough set ideology a bit different perspective by avoiding the usage of the knowledge granularity concept. Whereas the classical rough set theory is based on the indiscernibility relation (which can be equivalence, tolerance or other types of relations), we do not use such a notion and try to consider uncertainty from a logic-algebraic viewpoint.…”
Section: Algebraic Approach Versus Knowledge Granularitymentioning
confidence: 99%