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Experimental design
Author(s) -
Morgan J. P.,
Deng Xinwei
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1046
Subject(s) - computer science , key (lock) , selection (genetic algorithm) , data mining , latin hypercube sampling , machine learning , artificial intelligence , data science , mathematics , statistics , computer security , monte carlo method
Maximizing data information requires careful selection, termed design , of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Algorithmic Development > Statistics
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