An improved approach for sequential utility pattern mining

Guo Cheng Lan, Tzung Pei Hong*, S. Tseng, Shyue Liang Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we propose an efficient projection-based algorithm to discover high sequential utility patterns from quantitative sequence databases. An effective pruning strategy in the proposed algorithm is designed to tighten upper-bounds for subsequences in mining. By using the strategy, a large number of unpromising subsequences could be pruned to improve execution efficiency. Finally, the experimental results on synthetic datasets show the proposed algorithm outperforms the previously proposed algorithm under different parameter settings.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Pages226-230
Number of pages5
DOIs
StatePublished - 1 Dec 2012
Event2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China
Duration: 11 Aug 201213 Aug 2012

Publication series

NameProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012

Conference

Conference2012 IEEE International Conference on Granular Computing, GrC 2012
CountryChina
CityHangZhou
Period11/08/1213/08/12

Keywords

  • data mining
  • high sequence utility upper-bound patterns
  • high sequential utility patterns
  • upper-bound

Cite this

Lan, G. C., Hong, T. P., Tseng, S., & Wang, S. L. (2012). An improved approach for sequential utility pattern mining. In Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012 (pp. 226-230). [6468697] (Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012). https://doi.org/10.1109/GrC.2012.6468697