Abstract
This paper addresses the discovery of sequential patterns in very large databases. Most of the existing algorithms use lattice structures in the space search that are very demanding computationally. The output of these algorithms generates a large number of rules. The aim of this work is to create a swift algorithm for the discovery of sequential patterns with a low time complexity. In this work, we also want to define tools that allow us to simplify the work of the final user, by offering a new visualization of the sequences, while bypassing the analysis of thousands of association rules.
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References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings 11th International Conference Data Engineering, ICDE, pp. 3–14. IEEE Press, Los Alamitos (1995)
Agrawal, R., Srikan, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Databases, pp. 478–499 (1994)
Berry, M., Linoff, G.: Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley and Sons, Chichester (1997)
Cavique, L.: A Scalable Algorithm for the Market Basket Analysis. Journal of Retailing and Consumer Services, Special Issue on Data Mining in Retailing and Consumer Services (accepted paper, 2007)
Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, Pearson Education Inc. (2003)
Edmonds, J.: Optimum branchings. J. Research of the National Bureau of Standards 71B, 233–240 (1967)
Fulkerson, D.R.: Packing rooted directed cuts in a weighted directed graph. Mathematical Programming 6, 1–13 (1974)
Giudici, P.: Applied Data Mining: Statistical Methods for Business and Industry. John Wiley and Sons, Chichester (2003)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, United States, pp. 1–12 (2000)
Karp, R.M.: A simple derivation of Edmonds’ algorithm for optimum branchings. Network 1, 265 (1971)
Prinzie, A., Van den Poel, D.: Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models. In: Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/213, Ghent University, Faculty of Economics and Business (2003)
Silvestri, C.: Distributed and Stream Data Mining Algorithms for Frequent Pattern Discovery, Universit‘a Ca’ Foscari di Venezia, Dipartimento di Informatica, Dottorato di Ricerca in Informatica, Ph.D. Thesis TD-2006-4 (2006)
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)
Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42, 31–60 (2001)
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Cavique, L. (2007). A Network Algorithm to Discover Sequential Patterns. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_34
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DOI: https://doi.org/10.1007/978-3-540-77002-2_34
Publisher Name: Springer, Berlin, Heidelberg
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