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Incremental learning optimization on knowledge discovery in dynamic business intelligent systems

(2011) JOURNAL OF GLOBAL OPTIMIZATION. 51(2). p.325-344
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Abstract
As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. Experimental results validate the feasibility of the incremental learning optimization.
Keywords
Interesting knowledge, Coverage, Business information, Optimization, ATTRIBUTE REDUCTION, ROUGH SET-THEORY, Accuracy, Incremental learning, Rough set theory

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Citation

Please use this url to cite or link to this publication:

Chicago
Liu, Dun, Tianrui Li, Da Ruan, and Junbo Zhang. 2011. “Incremental Learning Optimization on Knowledge Discovery in Dynamic Business Intelligent Systems.” Journal of Global Optimization 51 (2): 325–344.
APA
Liu, Dun, Li, T., Ruan, D., & Zhang, J. (2011). Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. JOURNAL OF GLOBAL OPTIMIZATION, 51(2), 325–344.
Vancouver
1.
Liu D, Li T, Ruan D, Zhang J. Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. JOURNAL OF GLOBAL OPTIMIZATION. 2011;51(2):325–44.
MLA
Liu, Dun, Tianrui Li, Da Ruan, et al. “Incremental Learning Optimization on Knowledge Discovery in Dynamic Business Intelligent Systems.” JOURNAL OF GLOBAL OPTIMIZATION 51.2 (2011): 325–344. Print.
@article{2918440,
  abstract     = {As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. Experimental results validate the feasibility of the incremental learning optimization.},
  author       = {Liu, Dun and Li, Tianrui and Ruan, Da and Zhang, Junbo},
  issn         = {0925-5001},
  journal      = {JOURNAL OF GLOBAL OPTIMIZATION},
  keyword      = {Interesting knowledge,Coverage,Business information,Optimization,ATTRIBUTE REDUCTION,ROUGH SET-THEORY,Accuracy,Incremental learning,Rough set theory},
  language     = {eng},
  number       = {2},
  pages        = {325--344},
  title        = {Incremental learning optimization on knowledge discovery in dynamic business intelligent systems},
  url          = {http://dx.doi.org/10.1007/s10898-010-9607-8},
  volume       = {51},
  year         = {2011},
}

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