Abstract
We present the notion of sequential association rule and introduce Sequential Nuggets of Knowledge as sequential association rules with possible low support and good quality, which may be highly relevant to scientific knowledge discovery. Then we propose the algorithm SNK that mines some interesting subset of sequential nuggets of knowledge and apply it to an example of molecular biology. Unexpected nuggets that are produced may help scientists refine a rough preliminary classification. A first implementation in Java is freely available on the web.
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., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. Eleventh International Conference on Data Engineering, pp. 3–14 (1995)
Azé, J., Kodratoff, Y.: A study of the Effect of Noisy Data in Rule Extraction Systems. In: Proc. of the Sixteenth European Meeting on Cybernetics and Systems Research (EMCSR 2002) (2), pp. 781–786 (2002)
Bairoch, A., Apweiler, R., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M.J., Natale, D.A., O’Donovan, C., Redaschi, N., Yeh, L.S.: The Universal Protein Resource (UniProt), Nucleic Acids Res. 33, D154–159 (2005)
Finn, R.D., Mistry, J., Schuster-Backler, B., Griffiths-Jones, S., Hollich, V., Lassmann, T., Moxon, S., Marshall, M., Khanna, A., Durbin, R., Eddy, S.R., Sonnhammer, E.L.L., Bateman, A.: Pfam: clans, web tools and services. Nucleic Acids Research, Database Issue 34, D247–D251 (2006)
Froidevaux, C., Lisacek, F., Rance, B.: Mining sequential nuggets of knowledge UPS-LRI, Technical report (to appear)
Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A Survey. ACM Computing surveys 38(3), Article 9 (2006)
Lallich, S., Teytaud, O., Prudhomme, E.: Association rule interestingness: measure and statistical validation. In: Guillet, F., Hamilton, H.J. (eds.) Quality Measures in data Mining, Springer, Heidelberg, 2006 (to appear)
Masseglia, F., Tanasa, D., Trousse, B.: Web Usage Mining: Sequential Pattern Extraction with a Very Low Support. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 513–522. Springer, Heidelberg (2004)
Nikitin, F., Rance, B., Itoh, M., Kanehisa, M., Lisacek, F.: Using Protein Motif Combinations to Update KEGG Pathway Maps and Orthologue Tables. Genome Informatics 2, 266–275 (2004)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16, 1424–1440 (2004)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: SIGKDD 2002 (2002)
Wootton, J.C., Federhen, S.: Statistics of local complexity in amino acid sequences and sequence databases. Comput. Chem. 17, 149–163 (1993)
Yun, H., Ha, D., Hwang, B., Ryu, K.H.: Mining association rules on significant rare data using relative support. The Journal of Systems and Software 67, 181–191 (2003)
Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. In: Fisher, D. (ed.) Machine Learning Journal, special issue on Unsupervised Learning, vol. 42, pp. 31–60 (2001)
Zhang, T.: Association Rules. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 245–256. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Christine, F., Frédérique, L., Bastien, R. (2007). Extracting Sequential Nuggets of Knowledge. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_72
Download citation
DOI: https://doi.org/10.1007/978-3-540-74469-6_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74467-2
Online ISBN: 978-3-540-74469-6
eBook Packages: Computer ScienceComputer Science (R0)