Abstract
In this paper, we propose a novel framework, called a frequent network, to discover frequent itemsets and potentially frequent patterns by logical inference. We also introduce some new terms and concepts to define the frequent network, and we show the procedure of constructing the frequent network. We then describe a new method LAFN (Learning based on Associative Frequent Network) for mining frequent itemsets and potentially patterns, which are considered as a useful pattern logically over the frequent network. Finally, we present a useful application, classification with these discovered patterns from the proposed framework, and report the results of the experiment to evaluate our classifier on some data sets.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 207–216 (1993)
Amari, S.I.: Learning patterns and pattern sequences by self-organizing nets. IEEE Transactions on Computer 21(11), 1197–1206 (1972)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI-93, pp. 1022–1027 (1993)
Hebb, D.O.: The Organization of Behaviour. John Wiley, New York (1949)
Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79(8), 2554–2558 (1982)
Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: a machine learning library in C++. In: Tools with artificial intelligence, pp. 740–743 (1994)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on Multiple Class-Association Rules. IEEE Computer Society Press, Los Alamitos (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating calssification and association rule mining. In: KDD’98, New York, NY (Aug. 1998)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Quinlan, J.R.: C4.5: Programs for machine Learning. Morgan Kaufmann, San Francisco (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Jung, JG., Uddin, M.N., Jo, GS. (2007). Learning Using a Self-building Associative Frequent Network. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-71629-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
Online ISBN: 978-3-540-71629-7
eBook Packages: Computer ScienceComputer Science (R0)