Abstract
Mining frequent partial orders from a collection of sequences was introduced as an alternative to mining frequent sequential patterns in order to provide a more compact/understandable representation. The motivation was that a single partial order can represent the same ordering information between items in the collection as a set of sequential patterns (set of totally ordered sets of items). However, in practice, a discovered set of frequent partial orders is still too large for an effective usage. We address this problem by proposing a method for ranking partial orders with respect to significance that extends our previous work on ranking sequential patterns. In experiments, conducted on a collection of visits to a website of a multinational technology and consulting firm we show the applicability of our framework to discover partial orders of frequently visited webpages that can be actionable in optimizing effectiveness of web-based marketing.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1) (2007)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE, pp. 3–14 (1995)
Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large datasets. In: SDM, pp. 166–177 (2003)
Guan, E., Chang, X., Wang, Z., Zhou, C.: Mining maximal sequential patterns. In: 2005 International Conference on Neural Networks and Brain, pp. 525–528 (2005)
Huang, X., An, A., Cercone, N.: Comparison of interestingness functions for learning web usage patterns. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, CIKM 2002, pp. 617–620. ACM, New York (2002)
Gwadera, R., Crestani, F.: Ranking sequential patterns with respect to significance. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS(LNAI), vol. 6118, pp. 286–299. Springer, Heidelberg (2010)
Mannila, H., Meek, C.: Global partial orders from sequential data. In: KDD 2000: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 161–168. ACM, New York (2000)
Casas-Garriga, G.: Summarizing sequential data with closed partial orders. In: Proceedings of the Fifth SIAM International Conference on Data Mining, April 2005, pp. 380–390 (2005)
Pei, J., Wang, H., Liu, J., Wang, K., Wang, J., Yu, P.S.: Discovering frequent closed partial orders from strings. IEEE Transactions on Knowledge and Data Engineering 18, 1467–1481 (2006)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q.: Mining sequential patterns by pattern-growth: The prefixspan approach. TKDE 16 (November 2004)
Gwadera, R., Atallah, M., Szpankowski, W.: Reliable detection of episodes in event sequences. In: Third IEEE International Conference on Data Mining, pp. 67–74 (November 2003)
Gwadera, R., Atallah, M., Szpankowski, W.: Markov models for discovering significant episodes. In: SIAM International Conference on Data Mining, pp. 404–414 (April 2005)
Atallah, M., Gwadera, R., Szpankowski, W.: Detection of significant sets of episodes in event sequences. In: Fourth IEEE International Conference on Data Mining, pp. 67–74 (October 2004)
Varol, Y.L., Rotem, D.: An algorithm to generate all topological sorting arrangements. The Computer Journal 24(1), 83–84 (1981)
Pruesse, G., Ruskey, F.: Generating linear extensions fast. SIAM J. Comput. 23, 373–386 (1994)
Knuth, D.E., Szwarcfiter, J.L.: A structured program to generate all topological sorting arrangements. Inf. Process. Lett
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
Gwadera, R., Antonini, G., Labbi, A. (2011). Mining Actionable Partial Orders in Collections of Sequences. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23780-5_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-23780-5_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23779-9
Online ISBN: 978-3-642-23780-5
eBook Packages: Computer ScienceComputer Science (R0)