Join and Meet Block Based Default Definite Decision Rule Mining from IDT and an Incremental Algorithm
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.
[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
[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
@article{"International Journal of Information, Control and Computer Sciences:59991", author = "Chen Wu and Jingyu Yang", title = "Join and Meet Block Based Default Definite Decision Rule Mining from IDT and an Incremental Algorithm", abstract = "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.", volume = "2", number = "12", pages = "4186-10", }