Approaches for Updating Approximations in Set-Valued Information Systems While Objects and Attributes Vary with Time

Part of the Intelligent Systems Reference Library book series (ISRL, volume 42)


Rough set theory is an important tool for knowledge discovery. The lower and upper approximations are basic operators in rough set theory. Certain and uncertain if-then rules can be unrevealed from different regions partitioned by approximations. In real-life applications, data in the information system are changing frequently, for example, objects, attributes, and attributes’ values in the information system may vary with time. Therefore, approximations may change over time. Updating approximations efficiently is crucial to the knowledge discovery. The set-valued information system is a general model of the information system. In this chapter, we focus on studying principles for incrementally updating approximations in a set-valued information system while attributes and objects are added. Then, methods for updating approximations of a concept in a set-valued information system is given while attributes and objects change simultaneously. Finally, an extensive experimental evaluation verifies the effectiveness of the proposed method.


Knowledge discovery rough set theory set-valued information system approximations 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Information Science and TechnologySouthwest Jiaotong University & Key Lab of Cloud Computing and Intelligent TechnologyChengduChina
  2. 2.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina

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