Applied Intelligence

, Volume 48, Issue 5, pp 1327–1343 | Cite as

Mining constrained inter-sequence patterns: a novel approach to cope with item constraints

  • Tuong Le
  • Anh Nguyen
  • Bao Huynh
  • Bay Vo
  • Witold Pedrycz


Data mining has become increasingly important in the Internet era. The problem of mining inter-sequence pattern is a sub-task in data mining with several algorithms in the recent years. However, these algorithms only focus on the transitional problem of mining frequent inter-sequence patterns and most frequent inter-sequence patterns are either redundant or insignificant. As such, it can confuse end users during decision-making and can require too much system resources. This led to the problem of mining inter-sequence patterns with item constraints, which addressed the problem when end-users only concerned the patterns contained a number of specific items. In this paper, we propose two novel algorithms for it. First is the ISP-IC (Inter-Sequence Pattern with Item Constraint mining) algorithm based on a theorem that quickly determines whether an inter-sequence pattern satisfies the constraints. Then, we propose a way to improve the strategy of ISP-IC, which is then applied to the \(i\)ISP-IC algorithm to enhance the performance of the process. Finally, pi ISP-IC, a parallel version of \(i\)ISP-IC, will be presented. Experimental results show that pi ISP-IC algorithm outperforms the post-processing of the-state-of-the-art method for mining inter-sequence patterns (EISP-Miner), ISP-IC, and \(i\)ISP-IC algorithms in most of the cases.


Data mining Pattern mining Inter-sequence pattern mining Constraint mining Parallel mining 



This research is funded by Foundation for Science and Technology Development of Ton Duc Thang University (FOSTECT), website:, under Grant FOSTECT.2015.BR.01.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tuong Le
    • 1
    • 2
  • Anh Nguyen
    • 3
  • Bao Huynh
    • 4
    • 5
  • Bay Vo
    • 6
  • Witold Pedrycz
    • 7
    • 8
    • 9
  1. 1.Division of Data ScienceTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  4. 4.Center for Applied Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Electrical Engineering and Computer ScienceVŠB-Technical University of OstravaOstrava-PorubaCzech Republic
  6. 6.Faculty of Information TechnologyHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam
  7. 7.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  8. 8.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia
  9. 9.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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