Skip to main content
SpringerLink
Log in

Study of Effective Mining Algorithms for Frequent Itemsets

  • Conference paper
  • First Online:
Intelligent Data Communication Technologies and Internet of Things

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 57))

Abstract

“Frequent Itemset Mining” is a domain where several techniques have been proposed in recent years. The most common techniques are tree-based, list-based, or hybrid approaches. Although each of these approaches was proposed with the intent of mining frequent itemsets efficiently, as the number of transactions increases, the performance of most of these algorithms gradually declines either in terms of time or memory. In addition, the presence of redundant itemsets is another crucial problem where a limited investigation has been carried out in recent years. There is thus a pressing need to develop more efficient algorithms that will address each of these concerns. This paper aims to survey the different approaches highlighting the advantages and disadvantages of each of them so that in future effective algorithms may be designed for extracting frequent items while addressing each of these concerns effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aryabarzan N, Minaei-Bidgoli B, Teshnehlab M (2018) negFIN: an efficient algorithm for fast mining frequent item sets. Expert Syst Appl 105:129–143

    Article  Google Scholar 

  2. Deng ZH (2018) DiffNodesets: an efficient structure for fast mining frequent item sets. Appl Soft Comput 48(9):214–223

    Google Scholar 

  3. Deng ZH, Lv SL (2014) Fast mining frequent item sets using Nodesets. Expert Syst Appl 41(10):4505–4512

    Article  Google Scholar 

  4. Kim J, Hwang B (2016) Real-time stream data mining based on CanTree and Gtree. Inf Sci (Ny) 367–368:512–528

    Article  Google Scholar 

  5. Meng Q, Sha J (2017) Tree-based frequent item sets mining for analysis of life-satisfaction and loneliness of retired athletes. Cluster Comput 20(4):3327–3335

    Article  Google Scholar 

  6. Tan L, Qin Q (2016) A new algorithm for fast mining frequent item sets based on SO-Sets. In: ICEICT, pp 342–346

    Google Scholar 

  7. Khawaja SG, Tehreem A, Akram MU (2017) Multicore framework for finding frequent item-sets using TDS. In: Proceedings of the 16th international conference on Hybrid Intelligent Systems (HIS 2016), vol 552 (no. His 2016)

    Google Scholar 

  8. Francia M, Golfarelli M, Rizzi S (2020) Summarization and visualization of multi-level and multi-dimensional itemsets. Inf Sci 520:63–85

    Article  MathSciNet  Google Scholar 

  9. Shah A, Halim Z (2019) On efficient mining of frequent itemsets from big uncertain databases. J Grid Comput 17(4):831–850

    Article  Google Scholar 

  10. Deng Z, Wang Z, Jiang J (2012) A new algorithm for fast mining frequent item sets using N-lists. Sci China Inform Sci 55(9):2008–2030

    Article  Google Scholar 

  11. Huynh-Thi-Le Q, Le T, VO B, Le B (2015) An efficient and effective algorithm for mining top-rank-k frequent patterns. Expert Syst Appl 42(1):156–164

    Google Scholar 

  12. Bui H, Vo B, Nguyen H, Nguyen-Hoang TA, Hong TP (2018) A weighted N-list-based method for mining frequent weighted item sets. Expert Syst Appl 96:388–405

    Article  Google Scholar 

  13. Deng ZH, Lv SL (2015) PrePost+: an efficient N-lists-based algorithm for mining frequent item sets via children-parent equivalence pruning. Expert Syst Appl 42(13):5424–5432

    Article  Google Scholar 

  14. Vo B, Le T, Coenen F, Hong TP (2016) Mining frequent item sets using the N-list and subsume concepts. Int J Mach Learn Cybern 7(2):253–265

    Article  Google Scholar 

  15. Zhang R, Chen W, Hsu TC, Yang H, Chung YC (2017) ANG: a combination of Apriori and graph computing techniques for frequent item sets mining. J Supercomputer 1–16

    Google Scholar 

  16. Dewar S, Goal V, Bear D (2017) A hybrid framework for mining high-utility item sets in a sparse transaction database. Appl Intel 47(3):809–827

    Article  Google Scholar 

  17. Kalpana B, Natarajan R (2008) Incorporating heuristics for efficient search space pruning in frequent item set mining strategies. Curr Sci 94(1):97–101

    Google Scholar 

  18. Calder’s T, Goethals B (2007) Non-derivable item set mining. Data Min Knowl Disc 14(1):171–206

    Google Scholar 

  19. Xiong H, Tan PN, Kumar V (2006) Hyperclique pattern discovery. Data Min Knowl Disc 13(2):219–242

    Article  MathSciNet  Google Scholar 

  20. Koufakou A (2014) Mining non-derivable hypercliques. Knowl Inf Syst 41(1):77–99

    Article  Google Scholar 

  21. Calders T, Goethals B (2005) Depth-first non-derivable itemset mining. In Proceedings of the 2005 SIAM international conference on data mining. SocInd Appl Math 250–261

    Google Scholar 

  22. Hashem T, Rezaul Karim M, Samiullah M, Farhan Ahmed C (2017) An efficient dynamic superset bit-vector approach for mining frequent closed item sets and their lattice structure. Expert Syst Appl 67:252–271

    Google Scholar 

  23. Tran MT, Le B, Vo B (2015) Combination of dynamic bit vectors and transaction information for mining frequent closed sequences efficiently. Eng Appl Artif Intell 38:183–189

    Article  Google Scholar 

  24. Zaki MJ, Hsiao CJ (2005) Efficient algorithms for mining closed item sets and their lattice structure. IEEE Trans Knowl Data Eng 17(4):462–478

    Article  Google Scholar 

  25. Grahne G, Zhu J (2005) Fast algorithms for frequent item set mining using FP-trees. IEEE Trans Knowl Data Eng 17(10):1347–1362

    Article  Google Scholar 

  26. ur Rehman Z, Shahbaz M, Shaheen M, Guergachi A (2015) FPS-tree algorithm to find top-k closed item sets in data streams. Arab J Sci Eng 40(12):3507–3521

    Google Scholar 

  27. Rodríguez-González Y, Lezama F, Iglesias-Alvarez CA, Martínez-Trinidad JF, Carrasco-Ochoa JA, de Cote EM (2018) Closed frequent similar pattern mining: reducing the number of frequent similar patterns without information loss. Expert Syst Appl 96:271–283

    Article  Google Scholar 

  28. Le T, Vo B (2015) An N-list-based algorithm for mining frequent closed patterns. Expert Syst Appl 42(19):6648–6657

    Article  Google Scholar 

  29. Fumarola F, Lanotte PF, Ceci M, Malerba D (2016) CloFAST: closed sequential pattern mining using sparse and vertical id-lists. Knowl Inf Syst 48(2):429–463

    Article  Google Scholar 

  30. Vanahalli MK, Patil N (2019) An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets. Inf Sci 496:343–362

    Article  Google Scholar 

  31. Vanahalli MK, Patil N (2020) An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality. J King Saud Univ Comput Inf Sci

    Google Scholar 

  32. Li B, Pei Z, Qin K, Kong M (2018) TT-miner: topology-transaction miner for mining closed itemset. IEEE Access 7:10798–10810

    Article  Google Scholar 

  33. Muhonen J, Toivonen H (2006) Closed non-derivable itemsets. In: PKDD, Springer-Verlag, Berlin Heidelberg, 601–608 (LNAI 4213)

    Google Scholar 

  34. Yun U, Lee G (2016) Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst Appl 54:304–327

    Article  Google Scholar 

  35. Liu Z, Hu L, Wu C, Ding Y, Wen Q, Zhao J (2018) A novel process-based association rule approach through maximal frequent itemsets for big data processing. Futur Gener Comput Syst 81:414–424

    Article  Google Scholar 

  36. Necir H, Drias H (2015) A distributed maximal frequent itemset mining with multiagents system on bitmap join indexes selection. Int J Inf Technol Manage 14(2–3):201–214

    Google Scholar 

  37. Chung SM, Luo C (2008) Efficient mining of maximal frequent itemsets fromdatabases on a cluster of workstations. Knowl Inf Syst 16:359–391

    Google Scholar 

  38. Nguyen LT, Vu DB, Nguyen TD, Vo B (2020) Mining maximal high utility itemsets on dynamic profit databases. Cybern Syst 1–21

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

    P. P. Jashma Suresh, U. Dinesh Acharya & N. V. Subba Reddy

Authors
  1. P. P. Jashma Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U. Dinesh Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. V. Subba Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. P. Jashma Suresh .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Czech Technical University, Prague, Czech Republic

    Robert Bestak

  3. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jashma Suresh, P.P., Dinesh Acharya, U., Subba Reddy, N.V. (2021). Study of Effective Mining Algorithms for Frequent Itemsets. In: Hemanth, J., Bestak, R., Chen, J.IZ. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 57. Springer, Singapore. https://doi.org/10.1007/978-981-15-9509-7_41

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-9509-7_41

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9508-0

  • Online ISBN: 978-981-15-9509-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation