Abstract
The meningitis dataset has been used for extracting meningitis knowledge by learning and mining methods. This paper reports the result of extracting knowledge from this dataset by a novel learning method called LUPC that integrates separate-and-conquer rule induction with association rule mining. We first briefly introduce the basic ideas of LUPC then describe experiments, extracted knowledge and the result evaluation. The extracted knowledge is concerned with factors important for diagnosis (DIAG and DIAG2), for detection of bacteria or virus (CULT_FIND and CULTURE) and for predicting prognosis (C_COURSE and COURSE).
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
Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. International Conference Management of Data SIGMOD’93, 207–216.
Brunk, C. A.and Pazzani, M. J. (1991). An Investigation of Noise-Tolerant Relational Concept Learning Algorithms, Eight International Conference on Machine Learning, 389–393.
Furnkranz, J. (1999). Separate-and-Conquer Rule Learning. Journal Artificial Intelligence Review, 13, 3–54.
Tsumoto, S. (2000). Comparison and Evaluation of Knowledge Obtained by KDD Methods. Journal of Japanese Society for Artificial Intelligence, Vol. 15, N. 5, 790–797.
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
Ho, T.B., Kawasaki, S., Nguyen, D.D. (2001). Extracting Meningitis Knowledge by Integration of Rule Induction and Association Mining. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_71
Download citation
DOI: https://doi.org/10.1007/3-540-45548-5_71
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43070-4
Online ISBN: 978-3-540-45548-6
eBook Packages: Springer Book Archive