Rough sets

Pawlak, Zdzisław; Grzymała-Busse, Jerzy W.; Słowiński, Roman; Ziarko, Wojciech

doi:10.1145/219717.219791

Cited by 1,881 publications

(313 citation statements)

References 12 publications

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“…The relationship between the rough set theory and decision analysis is presented in [8]. The present state of the rough set theory and its further perspectives are discussed in [9].…”

Section: Some Remarks On Applicationsmentioning

confidence: 99%

Rough set approach to knowledge-based decision support

Pawlak

1997

European Journal of Operational Research

581

245

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Abstract. The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory -nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many real-life applications of the theory have proved its practical usefulness. The paper presents the basic assumptions underlying the rough sets philosophy, gives its fundamental concepts and discusses briefly some areas of applications, in particular in decision support. Finally further problems are shortly outlined.

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“…The relationship between the rough set theory and decision analysis is presented in [8]. The present state of the rough set theory and its further perspectives are discussed in [9].…”

Section: Some Remarks On Applicationsmentioning

confidence: 99%

Rough set approach to knowledge-based decision support

Pawlak

1997

European Journal of Operational Research

581

245

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“…However rough-set theory, which is a logicmathematical method proposed by Z. Pawlak, has shown to be an effective tool in analyzing this type of issues [2][3][4][5]. In recent years, this theory has been widely implemented in the many fields of natural science and societal science [6][7][8][9]10,[11][12][13]. In this paper, we establish an information system based on the above table.…”

Section: ) the Coincident Index Is The Index Reflecting The Current Bmentioning

confidence: 99%

An Applications of Information Systems on Macro-Economic Climate Index of China

He¹,

Ge²

2011

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Recently, National Bureau of Statistics of China has released macro-economic climate index of China from 2009-02 to 2010-05.Based on these indices, we establish an information system. In this information system, monitoring signal is taken as a decision attribute and coincident index, leading index, lagging index are taken as condition attributes. We use rough-set theory to investigate the importance of each condition attribute with respective to decision attribute and the strength of each condition attribute supporting decision attribute. Results of this investigation will be helpful for Chinese government to make active macro-economic policy and to maintain the steady and relatively fast development of Chinese economy.

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“…In the following section the notion of Non-Reduct is described. [13,15,16]. The very intuitive definition of Non-Reduct can be reflected as follows: Table 3 depicts the computation of Reducts based on a DS.…”

Section: Classmentioning

confidence: 99%

Fast outlier detection using rough sets theory

Shaari

Bakar

Hamdan

2008

WIT Transactions on Information and Communication Technologies

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In many Knowledge Discovery applications, finding outliers is more interesting than finding inliers in a dataset. The perception of outliers is rare cases in dataset in which is being described as abnormal data in the information table. Outliers detections are applied in many important applications like fraud detection systems to uncover the suspicious objects which may have important knowledge hidden in the system. A new outlier detection technique based on Rough Sets Theory (RST) is hereby proposed. RSetOF is a new measure for the outlier factor based on RST. By employing this factor, a new formulation for detecting outlier is established. The outlyingness of outliers objects in a dataset using this measurement is identified. To detect outliers, two measurements which are the top n ratio and the coverage ratio are presented. Finding top n outliers from all objects allow searching of outliers from top ranked records based on the least outlier factor value. The capability in detecting outliers at top n number of outliers will indicate how fast the detection is. The efficiency of this technique by obtaining the coverage ratio value is then tested. The maximum percentage of coverage obtained shows the maximum number of outliers detected belonging to rare cases. A comparison is hence carried out to examine the performance of the RSetAlg with a selective outlier detection method, the Frequent Pattern method referred to as FindFPOF. Ten benchmark datasets for assessing the outlier detection technique are used for this purpose. The experimental result shows that the proposed technique is competitive and proven to be better in speed of detection than the other technique. The fast and efficient detection of outliers has proven its potential as a new outliers detection technique based on RST.

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Rough sets

Cited by 1,881 publications

References 12 publications

Rough set approach to knowledge-based decision support

Rough set approach to knowledge-based decision support

An Applications of Information Systems on Macro-Economic Climate Index of China

Fast outlier detection using rough sets theory

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