| Abstract |
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Objective — To present the Knowledge Discovery in Serial Measures (KDSM) methodology as an easy and optimal way for analyzing repeated very short serial measures with a blocking factor. Method — An application to labor the domain is described using KDSM. Results — Novel knowledge about labor domain’s behavior was obtained once KDSM was applied to this specific domain. Conclusion — KDSM is a hybrid methodology (statistic and artificial intelligence) that gives a possible solution to a knowledge problem, especially when seemingly there are no relevant attributes.
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 Additional Information
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Index Terms- Knowledge Discovery and Labor Domain
Citation:
Jorge Rodas, Gabriela Alvarado, Fernando Vazquez,
"Using the KDSM Methodology for Knowledge Discovery from a Labor Domain,"
snpd-sawn,
pp. 64-69,
Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05),
2005
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