Applications of Rough Set Decompositions in Information Retrieval
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:61528", author = "Chen Wu and Xiaohua Hu", title = "Applications of Rough Set Decompositions in Information Retrieval", abstract = "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.", volume = "4", number = "3", pages = "567-6", }