Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32583
Applications of Rough Set Decompositions in Information Retrieval

Authors: Chen Wu, Xiaohua Hu


This paper proposes rough set models with three different level knowledge granules in incomplete information system under tolerance relation by similarity between objects according to their attribute values. Through introducing dominance relation on the discourse to decompose similarity classes into three subclasses: little better subclass, little worse subclass and vague subclass, it dismantles lower and upper approximations into three components. By using these components, retrieving information to find naturally hierarchical expansions to queries and constructing answers to elaborative queries can be effective. It illustrates the approach in applying rough set models in the design of information retrieval system to access different granular expanded documents. The proposed method enhances rough set model application in the flexibility of expansions and elaborative queries in information retrieval.

Keywords: Incomplete information system, Rough set model, tolerance relation, dominance relation, approximation, decomposition, elaborative query.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1542


[1] C.C.Chan,"A Rough Set Approach to Attribute Generalization in Data Mining",Information Sciences, 107, 1998, pp.169-176.
[2] C.Wu, X.B.Yang, "Information Granules in General and Complete Covering", Proceedings of 2005 IEEE International Conference on Granular Computing, pp.675-678.
[3] G.Li,X.Zhang, "Decomposition of Rough Set Based on Similarity Relation", J. of Computer Engineering and Applications, 2,2004,pp. 85-96,179.
[4] J.Stefanowski,A.Tsoukiàs, "Incomplete Information Tables and Rough Classification", J. Computational Intelligence, Vol. 17, 3 ,2001,pp.545-566.
[5] K. Funakoshi, T. B. Ho, "Information Retrieval by Rough Tolerance Relation", Proceedings of the 4th International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery, November 6-8, 1996, Tokyo, Japan, pp. 31-35.
[6] M.Kryszkiewicz, "Rough Set Approach to Incomplete Information Systems", Information Sciences, Vol.112,1,1998, pp.39-49.
[7] R.W. Winiarski,A.Skowron, "Rough Set Methods in Feature Selection and Recognition",Pattern Recognition Letters, 24,2003,pp.833~849.
[8] V.S.Ananthanarayana,M.N.Murty and D.K.Subramanian, "Tree Structure for Efficient Data Mining Using Rough Sets", Pattern Recognition Letters, 24,2003,pp.851-862.
[9] W.L.Chen,J.X.Cheng and C.J.Zhang, "A Generalization to Rough Set Theory Based on Tolerance Relation", J. computer eng ineering and applications, 16,2004,pp.26-28.
[10] Y.Li,"A Fuzzy-Rough Model for Concept Based Document Expansion", RSCTC 2004,LNAI 3066,pp.699-707.
[11] Z.Pawlak, "Rough sets and intelligent data analysis", Information Sciences. 147,2002,pp.1-12.