Abstract
The association rule mining problem is among the most popular data mining techniques. Association rules, whose significance is measured via quality indices, have been intensively studied for binary data. In this paper, we deal with association rules in the framework of categorical or tree-like-valued attributes.
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., IMIALINSKI, T., SWAMI, A. (1993): Mining association rules between sets of items in large databases. In: P. Buneman and S. Jajodia (Eds.): ACM SIGMOD International Conference on Management of Data. ACM press, Washington,207–216.
AGRAWAL, R., SRIKANT, R. (1994): Fast algorithms for mining association rules. In: B. Jorge, Bocca, M. Jarke, and C. Zaniolo, (Eds.): Proceed. of the 20 th VLDB Conference, 487–499.
BRITO, P. (1994): Order Structure of Symbolic Assertion Objects. IEEE Transactions on Knowledge and Data Engineering, 6(5), 830–835.
CONRUYT, N., GOSSER, D., RALAMBONDRAINY, H. (1997): IKBS: An Interative Knowledge Base System for improving description, Classification and identification of biological objects. Proceedings of the Indo-French Workshop on Symbolic Data Analysis and its Applications 2, 212–224.
DIATTA, J. (2006): Description-meet compatible multi-way dissimilarities. Discrete Applied Mathematics 154, 493–507.
DIATTA, J. (2007): Galois closed entity sets and k-balls of quasi-ultrametric multi-way dissimilarities, Advances in Data Analysis and Classification 1, 53–65.
DIATTA, J., RALAMBONDRAINY, H. (2002): The conceptual weak hierarchy associated with a dissimilarity measure. Mathematical Social Sciences 44, 301–319.
DIATTA, J., RALAMBONDRAINY, H., TOTOHASINA, A. (2007): Towards a unifying probabilistic implicative normalized quality measure for association rules. Book Series Studies in Computational Intelligence Springer Berlin/Heidelberg 43, 237–250.
FAYYAD, U.M., PIATETSKY-SHAPIRO, SMYTH, P. (1996): Knowledge discovery and data mining: towards a unifying framework. Proceedings of the second International Conference on Knowledge Discovery and Data Mining. Portland, OR, 82–88.
PASQUIER, N., BASTIDE, Y., TAOUIL, R., LAKHAL, L. (2000): Efficient mining of association rules using closed itemset lattices. Information Systems 24, 25–46.
ZAKI, M.J., OGIHARA, M. (1998): Theoretical foundations of association rules. 3rd SIGMOD’98 Workshop on Research Issues in Data Mining and Knowledge Discovery, 1–8.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ralambondrainy, H., Diatta, J. (2007). Association Rules for Categorical and Tree Data. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-73560-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73558-8
Online ISBN: 978-3-540-73560-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)