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Experimental design

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Abstract 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

Two orthogonal arrays.

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A four‐dimensional, nine‐level Latin hypercube and its two‐dimensional projections.

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Algorithmic Development > Statistics
Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining
Application Areas > Science and Technology

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