Information Technology Reference
In-Depth Information
3. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar,
S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169-194. Springer, Heidelberg
(2012)
4. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th
International Conference Data Engineering, ICDE, pp. 3-14. IEEE Press (1995)
5. Borges, J., Levene, M.: Evaluating Variable-Length Markov Chain Models for Analysis of
User Web Navigation Sessions. IEEE Trans. Knowl. Data Eng. 19(4), 441-452 (2007)
6. Cavique, L.: A Network Algorithm to Discover Sequential Patterns. In: Neves, J., Santos,
M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 406-414. Springer,
Heidelberg (2007)
7. Cavique, L.: A new taxonomy in Data Science. Maximus Report, section IV, pp. 92-93
(2014) (in Portuguese)
8. Cavique, L., Coelho, J.: Sequential Pattern Discovery Using Oriented Trees. Revista de
Ciências da Computação (3), 12-22 (2008) (in Portuguese)
9. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd
edn. MIT Press and McGraw-Hill (2009) ISBN 0-262-03384-4
10. Edmonds, J.: Optimum branchings. J. Research of the National Bureau of Standards 71B,
233-240 (1967)
11. Fulkerson, D.R.: Packing rooted directed cuts in a weighted directed graph. Mathematical
Programming 6, 1-13 (1974)
12. GraphViz (2014), http://www.graphviz.org/ (accessed June 9, 2014)
13. IBM Almaden Research Center, Synthetic data generation code for associations and
sequential patterns (2006), http://www.almaden.ibm.com/software/quest/
14. Knuth, D.E., Morris, J.H., Pratt, V.R.: Fast pattern matching in strings. SIAM Journal on
Computing 6(1), 323-350 (1977)
15. Mannila, H., Toivonen, H., Verkamo, I.: Discovery of frequent episodes in event
sequences. Data Mining and Knowledge Discovery 1(3), 259-289 (1997)
16. Marques, N.C., Cavique, L.: Sequential pattern mining of price interactions. In: EPIA
2013, 16th Portuguese Conference, Advances in Artificial Intelligence, Local Proceedings,
Angra do Heroísmo, Açores, Portugal, pp. 314-325 (2013)
17. McKendrick, J.: Pervasive Business Intelligence means BI for the masses. Informatica (2008),
http://blogs.informatica.com/perspectives/2008/08/31/
pervasive-business-intelligence-means-bi-for-the-
masses/#fbid=k4I_3ZvQSUp (accessed December 15, 2014)
18. Rebane, G., Pearl, J.: The recovery of causal poly-trees from statistical data. In:
Proceedings of Uncertainty in Artificial Intelligence, pp. 222-228 (1987)
19. 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)
20. Tiple, P.S.: Tool for Discovering Sequential Patterns in Financial Markets. Dissertação
para obtenção do Grau de Mestre em Engenharia Informática, na Faculdade de Ciências e
Tecnologia da Universidade Nova Lisboa (2014)
21. Tulip, Better Visualization Through Research (2014), http://tulip.labri.fr/
TulipDrupal (accessed December 2014)
22. Wang, W., Yang, J., Yu, P.: Meta-Patterns: revealing hidden periodic patterns. In: IEEE
International Conference on Data Mining (ICDM), pp. 550-557 (2001)
23. Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine
Learning 42, 31-60 (2001)
Search WWH ::




Custom Search