Abstract
The discovery of previously unknown and valuable knowledge is a key aspect of the exploitation of the enormous amount of data that stored in data sources. It has a wide range of applications, such as finance data analysis. A number of approaches for knowledge discovery and data mining have been developed for different scenarios. This paper presents our approach of integrating agent technology with conventional data mining methods to construct a system for financial data mining.
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
Kargupta, H., Park, B.-H.: MobiMine: Monitoring the Stock Market from a PDA. ACM 3 (2002)
Woodridge, M.: This is MyWorld: the logic of an agent-oriented testbed for DAI. In: Wooldridge, M.J., Jennings, N.R. (eds.) ECAI 1994 and ATAL 1994. LNCS, vol. 890, pp. 160–178. Springer, Heidelberg (1995)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
Maes, P.: Agents that Reduce Work and Information Overload. Communications of the ACM 37, 31–40 (1994)
El Adawy, M.I., Aboul-Wafa, M.E.: A SOFT-back propagation algorithm for training neural networks. In: Radio Science Conference, pp. 397–404 (2002)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI Press/MIT Press (1996)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with JAVA implementations. Morgan Kaufmann Publishers, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, W., Lü, K. (2004). Agent-Based Data Mining Approach for the Prediction of UK and Irish Companies Performance. In: Williams, H., MacKinnon, L. (eds) Key Technologies for Data Management. BNCOD 2004. Lecture Notes in Computer Science, vol 3112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27811-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-27811-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22382-5
Online ISBN: 978-3-540-27811-5
eBook Packages: Springer Book Archive