A novel three-way decision model based on incomplete information system

2016 ◽  
Vol 91 ◽  
pp. 32-45 ◽  
Author(s):  
Dun Liu ◽  
Decui Liang ◽  
Changchun Wang
2021 ◽  
pp. 1-13
Author(s):  
Jing Pang ◽  
Bingxue Yao ◽  
Lingqiang Li

In this paper, we point out that Lin’s general neighborhood systems-based rough set model is an extension of Qian’s optimistic rough set model, and thus called optimistic general neighborhood systmes-based rough set model. Then we present a new rough set model based on general neighborhood systems, and prove that it is an extension of Qian’s pessimistic rough set model. Later, we study the basic properties of the proposed pessimistic rough sets, and define the serial, reflexive, symmetric, transitive and Euclidean conditions for general neighborhood systems, and explore the further properties of related rough sets. Furthermore, we apply the pessimistic general neighborhood systems-based rough set model in the research of incomplete information system, and build a three-way decision model based on it. A simple practical example to show the effectiveness of our model is also presented.


2014 ◽  
Vol 687-691 ◽  
pp. 1500-1503
Author(s):  
Yong Lin Leng

With the development of information technology and data collection capabilities improve, the amount of data accumulated increase, missing data problems are more and more obvious. Traditional clustering methods can not cluster data set which contained missing data directly. In this paper, we proposed a novel missing data measurement method based on the incomplete information system theory and designed the similarity measure criterion for the discrete and successive of attributes separately. The experiment uses K-means clustering to test algorithm accuracy from different missing data rate and different amount of data two aspects, results demonstrate that the method can cluster missing data set efficiently and accurately.


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