Abstract
Sequential pattern mining is an important tool for solving many data mining tasks and it has broad applications. However, only few efforts have been made to extract this kind of patterns in a textual database. Due to its broad applications in text mining problems, finding these textual patterns is important because they can be extracted from text independently of the language. Also, they are human readable patterns or descriptors of the text, which do not lose the sequential order of the words in the document. But the problem of discovering sequential patterns in a database of documents presents special characteristics which make it intractable for most of the apriori-like candidate-generation-and-test approaches. Recent studies indicate that the pattern-growth methodology could speed up the sequential pattern mining. In this paper we propose a pattern-growth based algorithm (DIMASP) to discover all the maximal sequential patterns in a document database. Furthermore, DIMASP is incremental and independent of the support threshold. Finally, we compare the performance of DIMASP against GSP, DELISP, GenPrefixSpan and cSPADE algorithms.
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
Fayyad, U., Piatetsky-Shapiro, G.: Advances in Knowledge Discovery and Data mining. AAAI Press, Menlo Park (1996)
Feldman, R., Dagan, I.: Knowledge Discovery in Textual Databases (KDT). In: Proceedings of the 1st International Conference on Knowledge Discovery, KDD 1995 (1995)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: 5thIntl. Conf. Extending Database Discovery and Data Mining (1996)
Pei, J., Han, J.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proc. International Conference on Data Engineering, ICDE 2001 (2001)
Antunes, C., Oliveira, A.: Generalization of Pattern-growth Methods for Sequential Pattern Mining with Gap Constraints. In: Third IAPR Workshop on Machine Learning and Data Mining MLDM 2003 (2003)
Lin, M.-Y., Lee, S.-Y., Wang, S.-S.: DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 189–209. Springer, Heidelberg (2002)
http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
Zaki, M.J.: Sequence Mining in Categorical Domains: Incorporating Constraints. In: 9th International Conference on Information and Knowledge Management, Washington, DC, November 2000, pp. 422–429 (2000)
Youssefi, A.H., Duke, D.J., Zaki, M.J.: Visual Web Mining. In: 13th International World Wide Web Conference, New York, NY (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000) c.9 &10
Pei, J., Han, J., et al.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16(10) (October 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A. (2006). A New Algorithm for Fast Discovery of Maximal Sequential Patterns in a Document Collection. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_53
Download citation
DOI: https://doi.org/10.1007/11671299_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
Online ISBN: 978-3-540-32206-1
eBook Packages: Computer ScienceComputer Science (R0)