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Join and Meet Block Based Default Definite Decision Rule Mining from IDT and an Incremental Algorithm

Authors: Chen Wu, Jingyu Yang


Using maximal consistent blocks of tolerance relation on the universe in incomplete decision table, the concepts of join block and meet block are introduced and studied. Including tolerance class, other blocks such as tolerant kernel and compatible kernel of an object are also discussed at the same time. Upper and lower approximations based on those blocks are also defined. Default definite decision rules acquired from incomplete decision table are proposed in the paper. An incremental algorithm to update default definite decision rules is suggested for effective mining tasks from incomplete decision table into which data is appended. Through an example, we demonstrate how default definite decision rules based on maximal consistent blocks, join blocks and meet blocks are acquired and how optimization is done in support of discernibility matrix and discernibility function in the incomplete decision table.

Keywords: rough set, incomplete decision table, maximalconsistent block, default definite decision rule, join and meet block.

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[1] W.L. Chen, J.X. Cheng, C.J. Zhang, "A Generalized Model of Rough Set Theory Based on Compatibility Relation", Computer Engineering and Applications", Vol.40,No. 4, 2004,pp.26-28
[2] T.P. Hong, L.H. Tseng, S.L Wang, "Learning rules from incomplete training examples by rough sets", Expert Systems with Applications Vol. 22,Issue 4,2002, pp.285-293
[3] T.R. Li, N. Yang, Y. Xu, J. Ma, "An Incremental Algorithm for Mining Classification Rules in Incomplete Information System", Fuzzy Information, Processing NAFIPS '04. Vol. 1,2004, pp. 446- 449
[4] Y. Li, S. C.K. Shiu, S.K. Pal, J.N.K. Liu, "A Fuzzy-Rough Mothod for Concept Based Document Expansion", in Tsumoto,S. Slowinski,R. Komorowski,J. Grzymala-Busse,J.W.(Ed.), Rough Sets and Current Trends in Computing, Springer-Verlag Berlin Heidelberg,LNAI 3066,2004,pp.699-707
[5] G. Li, X. Zhang, "Decomposition of Rough Set Based on Similarity Relation", Computer Engineering and Applications, Vol.40,No.2,2004, pp.85-86 and pp.119
[6] Y. Leung, D. Li , "Maximal Consistent Block Technique for Rule Acquisition in Incomplete Information Systems" Information Sciences, Vol.153, No.1, 2003, pp: 85-106
[7] K. Funakoshi, T. B. Ho,"Information Retrieval by Rough Tolerance Relation", Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery, November 6-8, Tokyo, Japan, 1996,pp.31-35
[8] M. Kryszkiewicz, "Rough Set Approach to Incomplete Information Systems", J. of Information Sciences,Vol.112, No.1,1998, pp.39-49
[9] M. Kryszkiewicz, "Rules in Incomplete Information Systems",J. of information Sciences,Vol.113,1999, pp.271-292
[10] J.S. Mi, W.Z. Wu, W.X. Zhang, "Approaches to Knowledge Reduction Based on Variable Precision Rough Set Model", Information Sciences, Vol.159,No.3-4,2004,pp.255-272
[11] Z. Pawlak, "Rough sets and intelligent data analysis",Information Sciences. Vol.147,Issue 1-4, 2002 ,pp.1-12
[12] J. Stefanowski, A. Tsoukiàs, "Incomplete Information Tables and Rough Classification", J. Computational Intelligence, Vol. 11, No.3,2001,pp.545-566
[13] C. Wu,, X.B. Yang, "Information Granules in General and Complete Covering",Proceedings of 2005 IEEE International Conference on Granular Computing, Vol.2 , 2005,pp.675-678
[14] C. Wu,, X.H. Hu,J.Y Yang, X.B. Yang ,"Expanding Tolerance RST Models Based on Cores of Maximal Compatible Blocks", Rough Sets and Current Trends in Computing, Springer-Verlag Berlin Heidelberg,LNAI 4259, 2006,pp.235-243
[15] Y.Y. Yao, "Neigborhood systems and approximate retrieval",J. of Information Sciences,Vol.116,No.23,2006,pp.3431-3452