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CLC number: TP18
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Received: 2001-11-03
Revision Accepted: 2002-01-28
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Meta-information generation in distributed information system
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SU Jian, GAO Ji. Meta-information generation in distributed information system[J]. Journal of Zhejiang University Science A, 2002, 3(5): 532-537.
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author="SU Jian, GAO Ji",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2002.0532"
}
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Abstract: The authors discuss the concept of meta-information which is the description of information system or its subsystems, and proposes algorithms for meta-information generation. meta-information can be generated in parallel mode and network computation can be used to accelerate meta-information generation. Most existing rough set methods assume information system to be centralized and cannot be applied directly in distributed information system. Data integration, which is costly, is necessary for such existing methods. However, meta-information integration will eliminate the need of data integration in many cases, since many rough set operations can be done straightforward based on meta-information, and many existing methods can be modified based on meta-information.
[1] Chan,C.C., 1998. A rough set approach to attribute generalization in data mining. Journal of Information Sciences. 107:169-176.
[2] Guan, J.W., Bell,D.A.,1998. Rough computational methods for information systems. Artificial Intelligence. 105:77-103.
[3] Grzymala-Busse, J.W., Ziarko, W., 2000. Data mining and rough set theory. Communications of the ACM, 43, 108-109
[4] Komorowski,J., Pawlak,Z., Polkowski,L., Skowron, A., 1999. Rough sets: A Tutorial. In: Pal, S.K., Skowron A. ed., Rough fuzzy hybridization. A new trend in decision-making. Springer, p.3-98.
[5] Pawlak, Z., 1991. Rough set: Theoretical Aspects of Reasoning About Data. Kluwer Academic, Dordrecht, The Netherlands.
[6] Slowinski,R., Vanderpooten,D.,2000. A generalized definition of rough approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering. 12(2):331-336.
[7] Susmaga, R., 1998. Parallel Computation of Reducts. In: Polkowski,L.and Skowron, A., (Eds), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, vol.1424. Springer-Verlag, Berlin, p.450-457.
[8] Su,J.,Gao,J., 2001. A meta-information based method for incremental rough set rule generation. Pattern Recognition and Artificial Intelligence, 14(4):428-433(in Chinese).
[9] Zhong,N., Dong,J.Z.,Ohsuga,S.,2001. Using rough sets with heuristics for feature selection. Journal of Intelligent Information Systems,16:199-214.
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