Skip to main content
SpringerLink
Log in

High utility itemset mining using path encoding and constrained subset generation

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

In this paper a two phase approach for high utility itemset mining has been proposed. In the first phase potential high utility itemsets are generated using potential high utility maximal supersets. The transaction weighted utility measure is used in ascertaining the potential high utility itemsets. The maximal supersets are obtained from high utility paths ending in the items in the transaction database. The supersets are constructed without using any tree structures. The prefix information of an item in a transaction is stored in the form of binary codes. Thus, the prefix information of a path in a transaction is encoded as binary codes and stored in the node containing the item information. The potential high utility itemsets are generated from the maximal supersets using a modified set enumeration tree. The high utility itemsets are then obtained from the set enumeration tree by calculating the actual utility by scanning the transaction database. The experiments highlight the superior performance of the system compared to other similar systems in the literature.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1994) “Fast algorithms for mining association rules.” In Proc. 20th int. conf. very large data bases. VLDB 1215:487–499

    Google Scholar 

  2. Agrawal, R., Imieliński, T., & Swami, A. (1993). “Mining association rules between sets of items in large databases.” In Acm sigmod record 22 (2). ACM: 207–216

  3. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Elsevier, New York

    MATH  Google Scholar 

  4. Dam T, Li K, Fournier-Viger P et al (2019) CLS-miner: efficient and effective closed high-utility itemset mining. Front Comput Sci 13:357–381. https://doi.org/10.1007/s11704-016-6245-4

    Article  Google Scholar 

  5. Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Disc 15(1):5586

    Article  MathSciNet  Google 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 Disc 8(1):53–87

    Article  MathSciNet  Google Scholar 

  7. Sethi, K. K., & Ramesh, D. (2017). “HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing.” The Journal of Supercomputing: 1–17

  8. Yao, H., Hamilton, H. J., & Butz, C. J. (2004). “A foundational approach to mining itemset utilities from databases.” In Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics: 482–486

  9. Ahmed CF, Tanbeer SK, Jeong BS, Lee YK (2009) Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans Knowl Data Eng 21(12):1708–1721

    Article  Google Scholar 

  10. Lan GC, Hong TP, Tseng VS (2014) An efficient projection-based indexing approach for mining high utility itemsets. Knowl Inf Syst 38(1):85–107

    Article  Google Scholar 

  11. Li YC, Yeh JS, Chang CC (2008) Isolated items discarding strategy for discovering high utility itemsets. Data Knowl Eng 64(1):198–217

    Article  Google Scholar 

  12. Liu Y, Liao WK, Choudhary AN (2005) A two-phase algorithm for fast discovery of high utility Itemsets. In PAKDD 3518:689–695

    Google Scholar 

  13. Tseng VS, Shie BE, Wu CW, Philip SY (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans Knowl Data Eng 25(8):1772–1786

    Article  Google Scholar 

  14. Tseng, V. S., Wu, C. W., Shie, B. E., & Yu, P. S. (2010). “UP-Growth: an efficient algorithm for high utility itemset mining.” In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining ACM: 253–262

  15. Fournier-Viger, P., Lin, J. C. W., Duong, Q. H., & Dam, T. L. (2016). “FHM+: faster high-utility itemset mining using length upperbound reduction.” In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer International Publishing: 115–127

  16. Krishnamoorthy S (2015) Pruning strategies for mining high utility itemsets. Expert Syst Appl 42(5):2371–2381

    Article  Google Scholar 

  17. Yun U, Ryang H, Ryu KH (2014) High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates. Expert Syst Appl 41(8):3861–3878

    Article  Google Scholar 

  18. Chan, R., Yang, Q., & Shen, Y. D. (2003). “Mining high utility itemsets.” In Data Mining ICDM Third IEEE International Conference on IEEE: 19–26

  19. Uday KR, Yashwanth RT, Fournier-Viger P, Toyoda M, Krishna RP, Kitsuregawa M (2019) Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility. In: Yang Q, Zhou ZH, Gong Z, Zhang ML, Huang SJ (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture notes in computer science, vol 11440. Springer, Cham

    Google Scholar 

  20. Nguyen LT, Nguyen P, Nguyen TD, Vo B, Fournier-Viger P, Tseng VS (2019) Mining high-utility itemsets in dynamic profit databases. Knowl-Based Syst 175:130–144

    Article  Google Scholar 

  21. Sethi KK, Ramesh D, Edla DR (2018) P-FHM+: parallel high utility itemset mining algorithm for big data processing. Procedia Comput Sci 132:918–927

    Article  Google Scholar 

  22. Arybarzan N, Bidgoli B, Reshnehlab M (2018) negFIN: an efficient algorithm for fast mining frequent itemsets. Expert Syst Appl 105:129–143

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Department, P.B.R V I T S, Kavali, Andhra Pradesh, India

    Vamsinath Javangula

  2. CSE Department, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Andhra Pradesh, India

    Suvarna Vani Koneru

  3. CSE Department, University College of Engineering College, JNTUK, Kakinada, Andhra Pradesh, India

    Haritha Dasari

Authors
  1. Vamsinath Javangula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suvarna Vani Koneru
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haritha Dasari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vamsinath Javangula.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications

Guest Editor: Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javangula, V., Koneru, S. & Dasari, H. High utility itemset mining using path encoding and constrained subset generation. Peer-to-Peer Netw. Appl. 14, 2410–2418 (2021). https://doi.org/10.1007/s12083-020-00980-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00980-9

Keywords

Search

Navigation