Abstract
Incremental Maintenance of Frequent Itemsets (IMFI) consists in maintaining a set of extracted patterns when mined data are updated. This field knew considerable improvement in the last decade. However, it is not sufficiently tackled when mined data are imperfect, especially where imperfection is modelled by the evidence theory. In this work, we maintain incrementally the set of initially extracted itemsets both in cases of insertion and deletion of evidential data. Experimentations led on our method show satisfying results.
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.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Bach Tobji, M.-A., Ben Yaghlane, B., Mellouli, K.: A New Algorithm for Mining Frequent Itemsets from Evidential Databases. In: Proceedings of the 12th International Conference on IPMU, Malaga, Spain, pp. 1535–1542 (2008)
Bach Tobji, M.-A., Ben Yaghlane, B., Mellouli, K.: Incremental Maintenance of Frequent Itemsets in Evidential Databases. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 457–468. Springer, Heidelberg (2009)
Chen, G., Wei, Q.: Fuzzy association rules and the extended mining algorithms. Information Sciences-Informatics and Computer Science 147(1-4), 201–228 (2002)
Cheung, D.-W., Han, H., Ng, N.-T.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Update technique. In: Proceedings of The Twelfth International Conference on Data Engineering, pp. 106–114 (1996)
Dempster, A.P.: Upper and Lower Probabilities Induced by a Multivalue Mapping. Annals of Mathematical Statistics 38(2), 325–339 (1967)
Dai, B., Bell, D.A., Hughes, J.G.:Query processing in temporal evidential databases. In: Proceeding of the 24th Seminar on Current Trends in Theory and Practice of Informatics Milovy, Czech Republic, pp. 383–390 (1997)
Djouadi, Y., Redaoui, S., Amroun, K.: Mining Association Rules under Imprecision and Vagueness: towards a Possibilistic Approach. In: Intl. Fuzzy Systems Conference, vol. (23-26), pp. 1–6 (2007)
Hewawasam, K.K.R.G.K., Premaratne, K., Subasingha, S.P., Shyu, M.-L.: Rule Mining and Classification in Imperfect Databases. In: Proceedings of the Seventh International Conference on Information Fusion, pp. 661–668 (2005)
Leung, C.K.-S., Carmichael, C.L., Hao, B.: Efficient Mining of Frequent Patterns from Uncertain Data. In: Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 489–494 (2007)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Smets, P.: The Application of the Transferable Belief Model to Diagnostic Problems. Int. J. Intelligent Systems 13, 127–158 (1998)
Teng, W.-G., Chen, M.-S.: Incremental Mining on Association Rules. Studies in Fuzziness and Soft Computing, 125–162 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tobji, M.A.B., Yaghlane, B.B. (2010). Maintaining Evidential Frequent Itemsets in Case of Data Deletion. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-14055-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14054-9
Online ISBN: 978-3-642-14055-6
eBook Packages: Computer ScienceComputer Science (R0)