Abstract
Both the number and complexity of Data Mining projects has increased in late years. Unfortunately, nowadays there isn’t a formal process model for this kind of projects, or existing approaches are not right or complete enough. In some sense, present situation is comparable to that in software that led to ’software crisis’ in latest 60’s. Software Engineering matured based on process models and methodologies. Data Mining’s evolution is being parallel to that in Software Engineering. The research work described in this paper proposes a Process Model for Data Mining Projects based on the study of current Software Engineering Process Models (IEEE Std 1074 and ISO 12207) and the most used Data Mining Methodology CRISP-DM (considered as a “facto” standard) as basic references.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Naur, P., Randell, B.: Software engineering: Report on NATO conference (1969)
Piatetsky-Shaphiro, G., Frawley, W.: Knowledge Discovery in Databases. AAAI/MIT Press, MA (1991)
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 step-by-step data mining guide. Technical report, CRISP-DM (2000)
Eisenfeld, B., Kolsky, E., Topolinski, T.: 42 percent of CRM software goes unused (February 2003), http://www.gartner.com
Eisenfeld, B., Kolsky, E., Topolinski, T., Hagemeyer, D., Grigg, J.: Unused CRM software increases TCO and decreases ROI (Febrero 2003), http://www.gartner.com
Zornes, A.: The top 5 global 3000 data mining trends for 2003/04. META Group Research-Delta Summary, 2061 (March 2003)
Edelstein, H.A., Edelstein, H.C.: Building, Using, and Managing the Data Warehouse. In: Data Warehousing Institute, 1st edn., Prentice Hall PTR, Englewood Cliffs (1997)
Strand, M.: The Business Value of Data Warehouses - Opportunities, Pitfalls and Future Directions. PhD thesis, University of Skövde (December 2000)
Gondar, J.E.: Metodología Del Data Mining. Data Mining Institute, S.L (2005)
Pressman, R.: Software Engineering: A Practitioner’s Approach. McGraw-Hill, New York (2005)
Moore, J.: Software Engineering Standards: A User’s Road Map. IEEE, CA (1998)
Fayyad, U., Piatetsky-Shapiro, G., Smith, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, MA (1996)
Two Crows Corp. Introduction to Data Mining and Knowledge Discovery. 3rd edn. (1999)
SAS Institute. SEMMA data mining methodology (2005), http://www.sas.com
de Martínez Pisón, F.J.: Optimización Mediante Técnicas de Minería de Datos Del Ciclo de Recocido de Una Línea de Galvanizado. PhD thesis, Universidad de La Rioja (2003)
Solarte, J.: A proposed data mining methodoloy and its aplication to industrial engineering. Master’s thesis, University of Tennessee, Knoxville (2002)
IEEE. Standard for Developing Software Life Cycle Processes. IEEE Std. 1074-1997. IEEE Computer Society, Nueva York (EE.UU.) (1991)
ISO. ISO/IEC Standard 12207:1995. Software Life Cycle Processes. International Organization for Standarization, Ginebra (Suiza) (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marbán, Ó., Mariscal, G., Menasalvas, E., Segovia, J. (2007). An Engineering Approach to Data Mining Projects. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)