Abstract
In many application areas there are large historical data containing useful knowledge for decision support. However, this data taken in its raw form is usually of a poor quality. Thus it has very little value for the user-decision-maker if not adequately prepared. The Knowledge Discovery in Databases (KDD) is concerned with exploiting massive data sets in supporting use of historical data for decision-making. This paper describes an ongoing research project in the context of meteorological aviation forecasting, concerned with fog forecasting. The paper discusses the stages for performing knowledge discovery in the meteorological aviation-forecasting domain. The data used for research was taken from a real data set describing the aviation weather observations. The paper presents the data preprocessing stage, the discovered rules, achieved results and further directions of such research. We believe that this project can serve as a model for in a wider KDD-based decision support problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Auer, A.H.J. (1992). Guidelines for Forecasting Fog. Part 1: Theoretical Aspects: Meteorological Service of NZ.
Agrawal, R., Imielinski T.& Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of Conference Management of Data.
Beckenkamp, F., Pree, W.& Feldens, M.A. (1998). Optimizations of the Combinatorial Neural Model. In Proceedings of 5th Brazilian Symposium on Neural Networks (SBRN’98), Belo Horizonte, Brazil.
Buchner, A.G., Chan, J.C.L., Hung, S.L.& Hughes, J.G. (1998). A meteorological knowledgediscovery environment. Knowledge Discovery and Data Mining. (pp.204–226).
Catlett, J. (1991). Megainduction: Machine learning on very large databases. UTS, Australia.
Chen, F., Figlewski, S., Weigend, A.S.,& Waterhouse, S.R. (1998). Modeling financial data using clustering and tree-based approaches. In Proceedings of International Conference on Data Mining, (pp.35–51). Rio de Janeiro, Brazil: WIT Press.
Fayyad, U.M., Mannila, H.& Ramakrishman, R. (1997). Data Mining and Knowledge Discovery (Vol.3). Boston: Kluwer.
Gottgtroy, M.P.B., Rodrigues, M.J.N.& Sousa, M.T.G. (1998). Data mining agents. In Proc. of Intern.Conf on Data Mining (171–182). RiodeJaneiro, Brazil: WIT Press,Southampton, UK.
Howard, C.M. & Rayward-Smith, V.J. (1998). Discovering Knowledge from low-quality meteorological databases., Knowledge Discovery and Data Mining. (pp.180–202.).
Hruschka, E. & Ebecken, N. (1998). Rule Extraction from Neural Networks in Data Mining Applications. In Proc. of International Conference on Data Mining, (pp.303–314). RiodeJaneiro, Brazil: WIT Press, UK.
Keith, R. (1991). Results And Recommendations Arising From An Investigation Into Forecasting Problems At Melbourne Airport. (MeteorologicalNote 195). Townsville: Bureau of Meteorology, Meteorological Office.
Machado, R.J., Barbosa, V.C. & Neves, P.A. (1998). Learning in the Combinatorial Neural Model. IEEE Transactions on Neural Networks, 9. September 1998
Mohammed, J.Z., Parthasarathy S., Li W.& Ogihara, M. (1996). Evaluation of Sampling for Data Mining of Association Rules. (Tech.Rep. 617). Rochester, New York: The University of Rochester, Comp. Sci.Dept.
Piatetsky-Shapiro, G.,& Frawley, W. (1991). Knowledge Discovery in Databases.: MIT Press.
Provost, F.,& Kolluri, V. (1999, June, 1999). A Survey of Methods for Scaling Up Inductive Algorithms. Data Mining and Knowledge Discovery, Volume 3, 131–169.
Pyle, D. (1999). Data Preparation for Data Mining. San Francisco, USA: Morgan Kaufmann Publishers, Inc.
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. CA: Morgan Kaufmann.
Siegler, W. & Steurer, E. (1998). Forecasting of the German stock index DAX with neural networks: Using daily data for experiments with input variable reduction and a modified error function. In Proc. of International Conference on Data Mining. (pp.289–301). RiodeJaneiro, Brazil: WIT Press, Southampton, UK.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Viademonte, S., Burstein, F., Dahni, R., Williams, S. (2001). Discovering Knowledge from Meteorological Databases: A Meteorological Aviation Forecast Study. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2001. Lecture Notes in Computer Science, vol 2114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44801-2_7
Download citation
DOI: https://doi.org/10.1007/3-540-44801-2_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42553-3
Online ISBN: 978-3-540-44801-3
eBook Packages: Springer Book Archive