Abstract
Classification is a basic method of Data Mining. In this paper, we first introduce the basic concept of classifier and how to evaluate the precision of the classifier in this paper. Then we expatiate that how to use the Decision Tree Classifier to search the factors which will bring more venture at the guarantee slip, on the basis of the guarantee slip and compensation information database established by insurance agents. As a result, we gain some useful rules which will be useful to control investment venture.
Chapter PDF
Similar content being viewed by others
References
Heikki Mannnila, Hannu Toivonen and A. Inkeri. Verkamo, “Efficient algorithms for discov-ering association rules,” AAAI Workshop on Knowledge Discovery in Databases, pages 181–192, July 1994
K. Decker and S. Focardi, “Technology Overview: A Report on Data Mining,” ftp://ftp.cscs.ch/pub/CSCS/techreports
Tony Xiaohua Hu, “Knowledge Discovery in Databases: An Attribute-Oriented Rough Set Approach,” http://www.cs.bham.ac.uk/anpdm_docs
SGI Company, MineSet2.0 Tutorial
Gao Wen, “KDD: Knowledge Discovery in Databases,” Computer World, vol. 37, 1998
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tian, J., Zhang, S., Zhu, L., Li, B. (2003). Mining Investment Venture Rules from Insurance Data Based on Decision Tree. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44862-4_3
Download citation
DOI: https://doi.org/10.1007/3-540-44862-4_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40195-7
Online ISBN: 978-3-540-44862-4
eBook Packages: Springer Book Archive