Knowledge Discovery by Application of Rough Set Models

Stepaniuk, Jarosław

doi:10.1007/978-3-7908-1840-6_5

Cited by 70 publications

(23 citation statements)

References 82 publications

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“…The paper summarizes and extends some results presented in our previous works, presnted in particular in [32], [33], [34], [35], [36], [37], [38], [39], [43], [44], [45], [47], [48], [50].…”

Section: Fig 12 Classification Of Situationssupporting

confidence: 79%

Approximate Reasoning in Distributed Environments

Skowron

2004

Intelligent Technologies for Information Analysis

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Abstract. Information sources provide us with granules of information that must be transformed, analyzed and built into structures that support problem solving. Lotfi A. Zadeh has recently pointed out to the need to develop a new research branch called Computing with Words. One way to achieve Computing with Words is through Granular Computing (GC). The main concepts of GC are related to information granule calculi. One of the main goals of information granule calculi is to develop algorithmic methods for construction of complex information granules from elementary ones by means of available operations and inclusion (closeness) measures. These constructions can also be interpreted as approximate schemes of reasoning (AR-schemes). These constructed complex granules represent a form of information fusion. Such constructed granules should satisfy some constraints like quality criteria or/and degrees of granule inclusion in (closeness to) a given information granule. In the paper we discuss the idea of the rough neurocomputing paradigm for inducing AR-schemes based on rough sets and, in particular, on rough mereology. Information granule decomposition methods are important components of methods for AR-schemes induced from data and background knowledge. We report some recent results on information granule decomposition.

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Section: Fig 12 Classification Of Situationssupporting

confidence: 79%

Approximate Reasoning in Distributed Environments

Skowron

2004

Intelligent Technologies for Information Analysis

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“…The usual set-theoretical inclusion may be viewed as a special case of rough inclusion. Apart from the literature on rough mereology there are several papers where the problem of graded inclusion and, in particular, rough inclusion is addressed (see, e.g., [93,94,95,96,97,87,98]). …”

Section: Rough Inclusionmentioning

confidence: 99%

Satisfiability Judgement under Incomplete Information

Gomolińska

2010

Lecture Notes in Computer Science

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Abstract. In this paper we keep on discussing satisfiability of conditions by objects when information about the situation considered, including objects of some sort and concepts comprised of them, is incomplete. Our approach to satisfiability is that of concept modelling and we have a rough granular view on the problem. Objects considered are known partially, in terms of values of attributes of Pawlak information systems. An additional knowledge (domain knowledge) is assumed to be available. We choose descriptor languages for Pawlak information systems as specification languages in which we will express conditions about objects and concepts.

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“…In [4,9], the algorithms of finding the minimal relative reduct without using the discernibility matrix are presented.…”

Section: Induction Of Decision Rules Based Upon Rough Sets Theorymentioning

confidence: 99%

“…Application of rough set theory to data containing numerical attributes required their previous discretization [3] or tolerance based rough sets model use [9], in which the B-indiscernibility relation IND(B) is replaced by tolerance relation τ (B) (equivalence classes [x] IND(B) are replaced by tolerance sets I B (x))in the following way:…”

Section: Induction Of Decision Rules Based Upon Rough Sets Theorymentioning

confidence: 99%

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Fuzzy Decision Support System with Rough Set Based Rules Generation Method

Drwal

Sikora

2004

Rough Sets and Current Trends in Computing

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Abstract. This paper presents system which tries to combine the advantages of rough sets methods and fuzzy sets methods to get better classification. The fuzzy sets theory supports approximate reasoning and the rough sets theory is responsible for data analyzing and process of automatic fuzzy rules generation. The system was designed as a typical knowledge based system consisting of four main parts: rule extractor, knowledge base, inference engine, user interface and occurs to be useful tool in various decision problems and fuzzy control.

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Knowledge Discovery by Application of Rough Set Models

Cited by 70 publications

References 82 publications

Approximate Reasoning in Distributed Environments

Approximate Reasoning in Distributed Environments

Satisfiability Judgement under Incomplete Information

Fuzzy Decision Support System with Rough Set Based Rules Generation Method

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