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Mining Weighted Sequential Patterns Based on Length-Decreasing Support Constraints

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3975))

Abstract

We suggest an efficient weighted sequential pattern mining algorithm with length decreasing support constraints. Our approach is to push weight constraints and length decreasing support constraints to improve performance.

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References

  1. Masakazu, S., Karypis, G.: SLPMiner: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraints. In: ICDM 2002 (2002)

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  2. Yun, U., Leggett, J.J.: WFIM: Weighted Frequent Itemset Mining with a weight range and a minimum weight. In: SDM 2005 (April 2005)

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  3. Yun, U., Leggett, J.J.: WLPMiner: Weighted Frequent Pattern Mining with Length-decreasing support constraints. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 555–567. Springer, Heidelberg (2005)

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  4. Yun, U., Leggett, J.J.: WSpan: Weighted Sequential pattern mining in large sequence databases. In: IEEE IS 2006 (2006)

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© 2006 Springer-Verlag Berlin Heidelberg

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Yun, U., Leggett, J.J., Ong, T. (2006). Mining Weighted Sequential Patterns Based on Length-Decreasing Support Constraints. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_74

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  • DOI: https://doi.org/10.1007/11760146_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34478-0

  • Online ISBN: 978-3-540-34479-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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