Abstract
This work presents an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. XMuSer’s main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. XMuSer framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the most interesting sequential patterns into a new table, the sequence relation. In the last step of our framework, we use an ILP algorithm to learn a classification theory on the enlarged relational database that consists of the original multi-relational database and the new sequence relation.
We evaluate our framework by addressing three classification problems and map each one of three different types of sequential patterns: frequent, closed or maximal. The experiments show that our ILP based framework gains both from the descriptive power of the ILP algorithms and the efficiency of the sequential miners.
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
Blockeel, H., Sebag, M.: Scalability and efficiency in multi-relational data mining. SIGKDD Explorations 5(1), 17–30 (2003)
Costa, V.S.: The Life of a Logic Programming System. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 1–6. Springer, Heidelberg (2008)
Davis, J., Burnside, E., Ramakrishnan, R., Costa, V., Shavlik, J.: View learning for statistical relational learning: With an application to mammography. In: Proceeding of the 19th International Joint Conference on Artificial Intelligence, pp. 677–683. Professional Book Center, Edinburgh (2005)
Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)
Esposito, F., Di Mauro, N., Basile, T.M.A., Ferilli, S.: Multi-dimensional relational sequence mining. Fundamenta Informaticae 89(1), 23–43 (2009)
Ferreira, C.A., Gama, J., Costa, V.S.: Sequential Pattern Mining in Multi-relational Datasets. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds.) CAEPIA 2009. LNCS, vol. 5988, pp. 121–130. Springer, Heidelberg (2010)
Garofalakis, M., Rastogi, R., Shim, K.: Mining sequential patterns with regular expression constraints. IEEE Transactions on Knowledge and Data Engineering 14(3), 530–552 (2002)
Dan Lee, S., De Raedt, L.: Constraint Based Mining of First Order Sequences in SeqLog. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 154–173. Springer, Heidelberg (2004)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special Issue on Inductive Logic Programming 13(3&4), 245–286 (1995)
Muggleton, S., Feng, C.: Efficient induction of logic programs. In: First International Workshop on Algorithmic Learning Theory, pp. 368–381. Springer/Ohmsha, Tokyo, Japan (1990)
Novak, P.K., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal Machine Learning Research 10, 377–403 (2009)
Ohara, K., Yoshida, T., Geamsakul, W., Motoda, H., Washio, T., Yokoi, H., Takabayashi, K.: Analysis of Hepatitis Dataset by Decision Tree Graph-Based Induction (2004)
Quinlan, J.R., Cameron-Jones, R.M.: Induction of logic programs: Foil and related systems. New Generation Computing 13, 287–312 (1995)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Srinivasan, A.: The Aleph Manual (2003), http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph.html
Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large datasets. In: Proceedings of the Third SIAM International Conference on Data Mining, pp. 166–177. SIAM, San Francisco (2003)
Zaki, M.J.: Sequence mining in categorical domains: Incorporating constraints. In: CIKM, pp. 422–429 (2000)
Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 1(42), 31–60 (2001)
Zelezny, F., Lavrac, N.: Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62(1-2), 33–63 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreira, C.A., Gama, J., Santos Costa, V. (2012). Predictive Sequence Miner in ILP Learning. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-31951-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31950-1
Online ISBN: 978-3-642-31951-8
eBook Packages: Computer ScienceComputer Science (R0)