Abstract
In this paper a newclassification algorithm based upon frequent patterns is proposed. A frequent pattern is a generalization of the concept of a frequent item set, used in association rule mining. First of all, the collection of frequent patterns in the training set is constructed. For each frequent pattern, the support and the confidence is determined and registered. Choosing an appropriate data structure allows us to keep the full collection of frequent patterns in memory. The proposed classification method makes direct use of this collection. This method turns out to be competitive with a well-known classifier like C4.5 and other comparable methods. For large data sets it seems to be a very appropriate method.
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.
Reference
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo, Fast Discovery of Association Rules, Chapter 12 in: U.M. Fayyad et al. (eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, pp. 307–328, 1996.
A.V. Aho, J.E. Hopcroft and J.D. Ullman, Data Structures and Algorithms, pp. 163–169, ISBN 0-201-00023-7, Addison-Wesley Publishing Company, 1983.
E. Boros, T. Ibaraki, E. Mayoraz, P. Hammer, A. Kogan and I. Muchnik, An Implementation of Logical Analysis of Data, IEEE Transactions on Knowledge and Data Engineering, Vol. 12,No 2, pp. 292–306, March/April 2000.
W. Daelemans, A. van den Bosch and A. Weijters, IGTree: using trees for compression and classification in lazy learning algorithms, in: Artificial Intelligence Review 11, pp. 407–423, 1997.
U.M. Fayyad and K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, IJCAI-93, pp. 1022–1027.
Bin Liu, Wynn Hsu and Yiming Ma, Integrating Classification and Association Rule Mining, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York, 1998.
Bing Liu, Yiming Ma, and Ching-Kian Wong, Improving an Association Rule Based Classifier, Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2000) Lyon, France, LNAI 1910, pp. 504–509.
C.J. Merz and P. Murphy, UCI Repository of Machine Learning Databases, http://www.cs.uci.edu/~mlearn/MLRepository.html
Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht etc., 1991.
J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1992.
Ian H. Witten and Eibe Frank, Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, 2000.
W. Ziarko, Variable Precision Rough Set Model, J. of Computer and System Sciences, Vol. 46, pp. 39–59, 1993.
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
Pijls, W., Potharst, R. (2001). Classification Based upon Frequent Patterns. In: Kowalczyk, R., Loke, S.W., Reed, N.E., Williams, G.J. (eds) Advances in Artificial Intelligence. PRICAI 2000 Workshop Reader. PRICAI 2000. Lecture Notes in Computer Science(), vol 2112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45408-X_8
Download citation
DOI: https://doi.org/10.1007/3-540-45408-X_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42597-7
Online ISBN: 978-3-540-45408-3
eBook Packages: Springer Book Archive