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Incremental learning optimization on knowledge discovery in dynamic business intelligent systems

Dun Liu, Tianrui Li, Da Ruan UGent and Junbo Zhang (2011) JOURNAL OF GLOBAL OPTIMIZATION. 51(2). p.325-344
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Interesting knowledge, Coverage, Business information, Optimization, ATTRIBUTE REDUCTION, ROUGH SET-THEORY, Accuracy, Incremental learning, Rough set theory
journal title
JOURNAL OF GLOBAL OPTIMIZATION
J. Glob. Optim.
volume
51
issue
2
pages
325 - 344
Web of Science type
Article
Web of Science id
000294470200011
JCR category
MATHEMATICS, APPLIED
JCR impact factor
1.196 (2011)
JCR rank
53/245 (2011)
JCR quartile
1 (2011)
ISSN
0925-5001
DOI
10.1007/s10898-010-9607-8
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2918440
handle
http://hdl.handle.net/1854/LU-2918440
date created
2012-06-25 14:56:38
date last changed
2012-07-06 12:04:42
@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},
}

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.