A New Algorithm for Fast Discovery of Maximal Sequential Patterns in a Document Collection (original) (raw)

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.

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Authors and Affiliations

  1. National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, México
    René Arnulfo García-Hernández, José Francisco Martínez-Trinidad & Jesús Ariel Carrasco-Ochoa

Authors

  1. René Arnulfo García-Hernández
  2. José Francisco Martínez-Trinidad
  3. Jesús Ariel Carrasco-Ochoa

Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México
    Alexander Gelbukh

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© 2006 Springer-Verlag Berlin Heidelberg

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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

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