Abstract
Mining gradual rules plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form “The more/less X, then the more/less Y”. Such rules have been studied since the early 70’s, mostly in the fuzzy logic domain, where the main efforts have been focused on how to model and use such rules. However, mining gradual rules remains challenging because of the exponential combination space to explore. In this paper, we tackle the particular problem of handling huge volumes by proposing scalable methods. First, we formally define gradual association rules and we propose an original lattice-based approach. The GRITE algorithm is proposed for extracting gradual itemsets in an efficient manner. An experimental study on large-scale synthetic and real datasets is performed, showing the efficiency and interest of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 1–12 (1996)
Hüllermeier, E.: Association rules for expressing gradual dependencies. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 200–211. Springer, Heidelberg (2002)
Berzal, F., Cubero, J.C., Sanchez, D., Vila, M.A., Serrano, J.M.: An alternative approach to discover gradual dependencies. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 15(5), 559–570 (2007)
Dubois, D., Prade, H.: Gradual elements in a fuzzy set. Soft Comput. 12(2), 165–175 (2008)
Galichet, S., Dubois, D., Prade, H.: Imprecise specification of ill-known functions using gradual rules. International Journal of Approximate Reasoning 35, 205–222 (2004)
Dubois, D., Prade, H.: Gradual inference rules in approximate reasoning. Information Sciences 61(1-2), 103–122 (1992)
Jones, H., Dubois, D., Guillaume, S., Charnomordic, B.: A practical inference method with several implicative gradual rules and a fuzzy input: one and two dimensions. In: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007, IEEE International, pp. 1–6 (2007)
Bosc, P., Pivert, O., Ughetto, L.: On data summaries based on gradual rules. In: Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications, pp. 512–521. Springer, Heidelberg (1999)
Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 241–252. Springer, Heidelberg (2001)
Fiot, C., Masseglia, F., Laurent, A., Teisseire, M.: Gradual trends in fuzzy sequential patterns. In: 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (2008)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Data Bases (VLDB 1994), pp. 487–499 (1994)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: KDD 2002: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM, New York (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di-Jorio, L., Laurent, A., Teisseire, M. (2009). Mining Frequent Gradual Itemsets from Large Databases. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, JF. (eds) Advances in Intelligent Data Analysis VIII. IDA 2009. Lecture Notes in Computer Science, vol 5772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03915-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-03915-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03914-0
Online ISBN: 978-3-642-03915-7
eBook Packages: Computer ScienceComputer Science (R0)