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Soft Computing

, Volume 22, Issue 13, pp 4267–4273 | Cite as

Performance and characteristic analysis of maximal frequent pattern mining methods using additional factors

  • Gangin Lee
  • Unil Yun
Focus

Abstract

Various data mining methods have been proposed to handle large-scale data and discover interesting knowledge hidden in the data. Maximal frequent pattern mining is one of the data mining techniques suggested to solve the fatal problem of traditional frequent pattern mining approach. While traditional approach may extract an enormous number of pattern results according to threshold settings, maximal frequent pattern mining approach mines a smaller number of representative patterns, which allow users to analyze given data more efficiently. In this paper, we describe various recent maximal frequent pattern mining methods using additional factors and conduct performance evaluation in order to analyze their detailed characteristics.

Keywords

Data mining Knowledge discovery Maximal frequent pattern Pattern mining Representative pattern 

Notes

Acknowledgements

This study was funded by the Ministry of Education, Science and Technology of the National Research Foundation of Korea (NRF No. 20152062051 and NRF No. 20155054624).

Compliance with ethical standards

Conflicts of interest

Gangin Lee declares that he/she has no conflict of interest. Unil Yun declares that he has no conflict of interest.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: 20th international conference on very large data bases, pp 487–499Google Scholar
  2. Cho Y, Moon S (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FRAT analysis. J Converg 6(1):9–17CrossRefGoogle Scholar
  3. Gaur M, Pant B (2015) Trusted and secure clustering in mobile pervasive environment. Human-centric Comput Inf Sci 5(32):32:1–32:17Google Scholar
  4. Goparaju A, Brazier T, Salem S (2015) Mining representative maximal dense cohesive subnetworks. Netw Model Anal Health Inform Bioinform 4(1):29CrossRefGoogle Scholar
  5. Grahne G, Zhu Z (2005) Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans Knowl Data Eng 17(10):1347–1362CrossRefGoogle Scholar
  6. Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 8(1):53–87MathSciNetCrossRefGoogle Scholar
  7. Jeeva S, Rajsingh E (2016) Intelligent phishing url detection using association rule mining. Human-centric Comput Inf Sci 6(10):10:1–10:19Google Scholar
  8. Karim M, Rashid M, Jeong B, Choi H (2012) Privacy preserving mining maximal frequent patterns in transactional databases. In: 17th international conference on database systems for advanced applications, pp 303–319Google Scholar
  9. Lee G, Yun U, Ryang H, Kim D (2016) Approximate maximal frequent pattern mining with weight conditions and error tolerance. Int J Pattern Recognit Artif Intell 30(6):1650012:1–1650012:42CrossRefGoogle Scholar
  10. Li H, Zhang N (2016) Probabilistic maximal frequent itemset mining over uncertain databases. In: 21st international conference on database systems for advanced applications, pp 149–163Google Scholar
  11. Necir H, Drias H (2015) A distributed maximal frequent itemset mining with multi agents system on bitmap join indexes selection. Int J Inf Technol Manag 14(2/3):201–214Google Scholar
  12. Nikam S (2015) A comparative study of classification techniques in data mining algorithms. Orient J Comput Sci Technol 8(1):13–19Google Scholar
  13. Nourine L, Petit J (2016) Extended dualization: application to maximal pattern mining. Theor Comput Sci 618:107–121MathSciNetCrossRefzbMATHGoogle Scholar
  14. Salem S, Ozcaglar C (2013) MFMS: maximal frequent module set mining from multiple human gene expression data sets. in: 12th international workshop on data mining in bioinformatics, pp 51–57Google Scholar
  15. Sanna G, Angius A, Concas G, Manca D, Eros F (2015) PCE: a knowledge base of semantically disambiguated contents. J Converg 6(2):10–18Google Scholar
  16. Sato A, Huang R, Yen N (2015) Design of fusion technique-based mining engine for smart business. Human-centric Comput Inf Sci 5(23):23:1–23:16Google Scholar
  17. Stattner E, Collard M (2012) MAX-FLMin: an approach for mining maximal frequent links and generating semantical structures from social networks. In: 23rd international conference on database and expert systems applications, pp 468–483Google Scholar
  18. Wang F, Hu L, Zhou J, Hu J, Zhao K (2017) A semantics-based approach to multi-source heterogeneous information fusion in the internet of things. Soft Comput 21(8):2005–2013CrossRefGoogle Scholar
  19. Yun U, Lee G (2016) Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst Appl 54:304–327CrossRefGoogle Scholar
  20. Yun U, Ryu K (2013) Efficient mining of maximal correlated weight frequent patterns. Intell Data Anal 17(5):917–939Google Scholar
  21. Yun U, Lee G, Lee K (2016) Efficient representative pattern mining based on weight and maximality conditions. Expert Syst 33(5):439–462Google Scholar
  22. Zhang D, Niu H, Liu S (2016) Novel PEECR-based clustering routing approach. Soft Comput 1:1–11. doi: 10.1007/s00500-016-2270-3 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringSejong UniversitySeoulKorea

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