Approaches for Updating Approximations in Set-Valued Information Systems While Objects and Attributes Vary with Time
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.
KeywordsKnowledge discovery rough set theory set-valued information system approximations
Unable to display preview. Download preview PDF.
- 4.Chen, H.M., Li, T.R., Zhang, J.B.: A Method for incremental updating approximations when objects and attributes vary with time. In: Proceedings of 2010 IEEE International Conference on Granular Computing, Silicon Valley, pp. 90–95. IEEE Computer Society Press, Washington, DC (2010)CrossRefGoogle Scholar
- 5.Chen, H.M., Li, T.R., Zhang, J.B.: A Method for Incremental updating approximations based on variable precision set-valued ordered information systems. In: Proceedings of the 2010 IEEE International Conference on Granular Computing, Silicon Valley, pp. 96–101. IEEE Computer Society Press, Washington, DC (2010)CrossRefGoogle Scholar
- 23.Song, X.X., Zhang, W.X.: Knowledge reduction in inconsistent set-valued decision information system. Computer Engineering and Applications 45(1), 33–35 (2009)Google Scholar
- 25.Swiniarski, R.W., Pancerz, K., Suraj, Z.: Prediction of model changes of concurrent systems described by temporal information systems. In: Hamid, R. (ed.) Proceedings of the 2005 International Conference on Data Mining, Las Vegas, Nevada, USA, pp. 51–57. CSREA Press (2005)Google Scholar
- 26.Wang, G.Y.: Extension of rough set under incomplete information systems. Journal of Computer Research and Development 39(10), 1238–1243 (2002)Google Scholar
- 29.Zou, W.L., Li, T.R., Chen, H.M., Ji, X.L.: Approaches for incrementally updating approximations based on set-valued information systems while attribute values’ coarsening and refining. In: Proceedings of the 2009 IEEE International Conference on Granular Computing, pp. 824–829. IEEE Computer Society Press, Washington, DC (2009)CrossRefGoogle Scholar