Using the KDSM methodology for knowledge discovery from a labor domain

Author

Rodas, Jorge ; Alvarado, Gabriela ; Vázquez, Fernando

Author_Institution

Eng. Sch., ITESM, Chihuahua, Mexico

fYear

2005

fDate

23-25 May 2005

Firstpage

64

Lastpage

69

Abstract

The paper presents 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. An application to labor the domain is described using KDSM. A novel knowledge about labor domain´s behavior was obtained once KDSM was applied to this specific domain. 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.

Keywords

artificial intelligence; data mining; labour resources; statistics; KDSM; artificial intelligence; blocking factor; hybrid methodology; knowledge discovery; labor domain; serial measures; statistics; Application software; Artificial intelligence; Employment; Monitoring; Pattern analysis; Phase measurement; Production; Statistics; Time measurement; Time series analysis; Knowledge Discovery and Labor Domain;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2005 and First ACIS International Workshop on Self-Assembling Wireless Networks. SNPD/SAWN 2005. Sixth International Conference on

Print_ISBN

0-7695-2294-7

Type

conf

DOI

10.1109/SNPD-SAWN.2005.79

Filename

1434868