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Domain-Driven, Actionable Knowledge Discovery
July/August 2007 (vol. 22 no. 4)
pp. 78-88, c3
ASCII Text
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Longbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri, Eamonn Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco, "Domain-Driven, Actionable Knowledge Discovery," IEEE Intelligent Systems, vol. 22, no. 4, pp. 78-88, c3, July/August, 2007.
 
 
BibTex
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@article{ 10.1109/MIS.2007.67,
author = {Longbing Cao and Chengqi Zhang and Qiang Yang and David Bell and Michail Vlachos and Bahar Taneri and Eamonn Keogh and Philip S. Yu and Ning Zhong and Mafruz Zaman Ashrafi and David Taniar and Eugene Dubossarsky and Warwick Graco},
title = {Domain-Driven, Actionable Knowledge Discovery},
journal ={IEEE Intelligent Systems},
volume = {22},
number = {4},
issn = {1541-1672},
year = {2007},
pages = {78-88, c3},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2007.67},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Domain-Driven, Actionable Knowledge Discovery
IS - 4
SN - 1541-1672
SP78
EP88, c3
EPD - 78-88, c3
A1 - Longbing Cao,
A1 - Chengqi Zhang,
A1 - Qiang Yang,
A1 - David Bell,
A1 - Michail Vlachos,
A1 - Bahar Taneri,
A1 - Eamonn Keogh,
A1 - Philip S. Yu,
A1 - Ning Zhong,
A1 - Mafruz Zaman Ashrafi,
A1 - David Taniar,
A1 - Eugene Dubossarsky,
A1 - Warwick Graco,
PY - 2007
KW - data mining
KW - data models
KW - database searching
KW - knowledge engineering
KW - visualization
VL - 22
JA - IEEE Intelligent Systems
ER -
 
Longbing Cao, University of Technology, Sydney
Chengqi Zhang, University of Technology, Sydney
Qiang Yang, Hong Kong University of Science and Technology
David Bell, Queen's University Belfast
Michail Vlachos, IBM T.J. Watson Research Center
Bahar Taneri, Scripps Genome Center
Eamonn Keogh, University of California, Riverside
Philip S. Yu, IBM Thomas J. Watson Research Center
Ning Zhong, Maebashi Institute of Technology
Mafruz Zaman Ashrafi, Institute for Infocomm Research
David Taniar, Monash University
Eugene Dubossarsky, Ernst & Young
Warwick Graco, Australian Taxation Office
Existing knowledge discovery and data mining (KDD) field seldom deliver results that businesses can act on directly. This issue, Trends & Controversies presents seven short articles reporting on different aspects of domain-driven KDD, an R&D area that targets the development of effective methodologies and techniques for delivering actionable knowledge in a given domain, especially business.
Index Terms:
data mining, data models, database searching, knowledge engineering, visualization
Citation:
Longbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri, Eamonn Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco, "Domain-Driven, Actionable Knowledge Discovery," IEEE Intelligent Systems, vol. 22, no. 4, pp. 78-88, c3, July/Aug. 2007, doi:10.1109/MIS.2007.67
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