Abstract
Data mining techniques are used in order to discover emerging knowledge (patterns) in databases. The problem of such techniques is that there are, in general, too many resulting patterns for a user to explore them all by hand. Some methods try to reduce the number of patterns without a priori pruning. The number of patterns remains, nevertheless, high. Other approaches, based on a total ranking, propose to show to the user the top-k patterns with respect to a measure. Those methods do not take into account the user’s knowledge and the dependencies that exist between patterns. In this paper, we propose a new way for the user to explore extracted patterns. The method is based on navigation in a partial order over the set of all patterns in the Logical Concept Analysis framework. It accommodates several kinds of patterns and the dependencies between patterns are taken into account thanks to partial orders. It allows the user to use his/her background knowledge to navigate through the partial order, without a priori pruning. We illustrate how our method can be applied on two different tasks (software engineering and natural language processing) and two different kinds of patterns (association rules and sequential patterns).
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.
Similar content being viewed by others
References
Allard, P., Ferré, S., Ridoux, O.: Discovering functional dependencies and association rules by navigating in a lattice of OLAP views. In: Concept Lattices and Their Applications, pp. 199–210. CEUR-WS (2010)
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Int. Conf. on Management of Data. ACM Press, New York (1993)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Int. Conf. on Data Engineering. IEEE, Los Alamitos (1995)
Cellier, P., Charnois, T.: Fouille de données séquentielle d’itemsets pour l’apprentissage de patrons linguistiques. Traitement Automatique des Langues Naturelles (short paper) (2010)
Cellier, P., Ducassé, M., Ferré, S., Ridoux, O.: Formal concept analysis enhances fault localization in software. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 273–288. Springer, Heidelberg (2008)
Casas-Garriga, G.: Summarizing sequential data with closed partial orders. In: SIAM International Data Mining Conference (SDM) (2005)
Crémilleux, B., Soulet, A., Klema, J., Hébert, C., Gandrillon, O.: Discovering Knowledge from Local Patterns in SAGE data. IGI Publishing (2008)
Davey, B.A., Priestly, H.A.: Introduction to Lattices and Order, 2nd edn. Cambridge University Press, Cambridge (1990/2001)
Ferré, S.: Camelis: a logical information system to organize and browse a collection of documents. Int. J. General Systems 38(4) (2009)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence (1996)
Ferré, S., Ridoux, O.: An introduction to logical information systems. Information Processing & Management 40(3), 383–419 (2004)
Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Proc. of the Int. Conf. on Conceptual Structures: Broadening the Base, ICCS 2001, pp. 129–142. Springer, Heidelberg (2001)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Jay, N., Kohler, F., Napoli, A.: Analysis of social communities with iceberg and stability-based concept lattices. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 258–272. Springer, Heidelberg (2008)
Kontonasios, K., De Bie, T.: An information-theoretic approach to finding informative noisy tiles in binary databases. In: Proc. of the SIAM Int. Conf. on Data Mining, pp. 153–164 (2010)
Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8(1), 1–8 (2002)
Kuznetsov, S.O.: On stability of a formal concept. Annals of Mathematics and Artificial Intelligence. Springer Netherlands ACM (2007)
Marinica, C., Guillet, F.: Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans. Knowl. Data Eng. (2010)
Marinica, C., Olaru, A., Guillet, F.: User-driven association rule mining using a local algorithm. In: Int. Conf. on Enterprise Information Systems (ICEIS), vol. (2), pp. 200–205 (2009)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Plantevit, M., Crémilleux, B.: Condensed representation of sequential patterns according to frequency-based measures. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 155–166. Springer, Heidelberg (2009)
Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent itemsets with convertible constraints. In: Int. Conf. on Data Engineering. IEEE computer society, Los Alamitos (2001)
Richards, D., Malik, U.: Mining propositional knowledge bases to discover multi-level rules. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) MDM/KDD 2002 and KDMCD 2002. LNCS (LNAI), vol. 2797, pp. 199–216. Springer, Heidelberg (2003)
Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.): Visual Data Mining. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cellier, P., Ferré, S., Ducassé, M., Charnois, T. (2011). Partial Orders and Logical Concept Analysis to Explore Patterns Extracted by Data Mining. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds) Conceptual Structures for Discovering Knowledge. ICCS 2011. Lecture Notes in Computer Science(), vol 6828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22688-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-22688-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22687-8
Online ISBN: 978-3-642-22688-5
eBook Packages: Computer ScienceComputer Science (R0)