Skip to main content
SpringerLink
Log in

Review on High Utility Rare Itemset Mining

  • Conference paper
  • First Online:
Social Networking and Computational Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 100))

Abstract

Today’s era is an era of data. Hence, the most attentive field of research and study is to collect and select the data rapidly as per the need nowadays. Data mining is the field which helps industries to overcome the problem of data extraction. Association rule mining (ARM) is one of the major techniques of data mining which identifies the itemsets appear frequently in the dataset known as frequent itemset and generates the association rules. This helps in decision making. The extension of traditional association rule mining has come up with the concept of utility which should be considered while mining; hence, utility mining aims to identify the itemsets which not only have the frequent occurrences but also have considered the utility of the itemset. High utility itemsets mining can be used as an efficient method to discover interesting patterns. Rare items are items that appear fewer frequently in a database. High utility itemsets can be frequent or rare. Even rare itemsets can also be of high or low utility. In this paper, a literature survey of various research works has been discussed on High Utility Rare Itemset Mining. We have thoroughly surveyed High Utility Rare Itemset Mining methods and applications here. Also, we have focused on some open research issues to represent future challenges in this domain.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Abaya SA (2012) Association rule mining based on Apriori algorithm in minimizing candidate generation. Int J Sci Eng Res 3(7):1–4

    Google Scholar 

  2. Adda M, Wu L, Feng Y (2007) Rare itemset mining. In: IEEE sixth international conference on machine learning and applications, ICMLA 2007, pp 73–80

    Google Scholar 

  3. Adda M, Wu L, White S, Feng Y (2007) Pattern detection with rare item-set mining. arXiv preprint. arXiv:1209.3089

  4. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo (2007) AI Fast discovery of association rules. Adv Knowl Discov Data Min 12(1):307–328

    Google Scholar 

  5. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference very large data bases VLDB, vol 1215, pp 487–499

    Google Scholar 

  6. Ashrafi MZ, Taniar D, Smith K (1994) A new approach of eliminating redundant association rules. In: International conference on database and expert systems applications. Springer, Berlin, Heidelberg, pp 465–474S

    Google Scholar 

  7. Ashrafi MZ, Taniar D, Smith K (2007) Redundant association rules reduction techniques. Int J Bus Intell Data Min 2(1):29–63

    Article  MATH  Google Scholar 

  8. Cagliero L, Garza P (2014) Infrequent weighted itemset mining using frequent pattern growth. IEEE Trans Knowl Data Eng 26(4):903–915

    Article  Google Scholar 

  9. Chui CK, Kao B, Hung E (2007) Mining frequent itemsets from uncertain data. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 47–58

    Google Scholar 

  10. Dimitrijevic M, Bosnjak Z, Subotica S (2010) Discovering interesting association rules in the web log usage data. Interdiscip J Inf Knowl Manag 5:191–207

    Google Scholar 

  11. Duong QH, Liao B, Fournier-Viger P, Dam TL (2016) An efficient algorithm for mining the top-k high utility itemsets using novel threshold raising and pruning strategies. Knowl Based Syst 104:106–122

    Article  Google Scholar 

  12. Erwin A, Gopalan RP, Achuthan NR (2007) A bottom-up projection based algorithm for mining high utility itemsets. In: Proceedings of the 2nd international workshop on integrating artificial intelligence and data mining Australian computer society Inc., vol 84, pp 3–11

    Google Scholar 

  13. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 173:37

    Google Scholar 

  14. Fournier-Viger P, Lin JC, Vo B, Chi TT, Zhang J, Le HB (2017) A survey of itemset mining. Wiley Interdiscip Rev: Data Min Knowl Discov 7(4):e1207

    Google Scholar 

  15. Fournier-Viger P, Wu CW, Zida S, Tseng VS (2014) FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: International symposium on methodologies for intelligent systems. Springer, Cham, pp 83–92

    Google Scholar 

  16. Fournier-Viger P and Zida S (2015) FOSHU: faster on-shelf high utility itemset mining with or without negative unit profit. In: Proceedings of the 30th annual ACM symposium on applied computing, pp 857–864

    Google Scholar 

  17. Goyal V, Dawar S, Sureka A (2015) High utility rare itemset mining over transaction databases. In: International workshop on databases in networked information systems. Springer, Cham, pp 27–40

    Google Scholar 

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

    Article  Google Scholar 

  19. Gyenesei A (2000) Mining weighted association rules for fuzzy quantitative items. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 416–423

    Chapter  Google Scholar 

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

    Google Scholar 

  21. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. ACM Sigmod Rec ACM 29(2):1–12

    Article  Google Scholar 

  22. Hu YH, Chen YL (2006) Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decis Support Syst 42(1):1–24

    Article  Google Scholar 

  23. Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. Wiley

    Google Scholar 

  24. Kiran RU, Reddy PK (2011) Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: Proceedings of the 14th international conference on extending database technology ACM, pp 11–20

    Google Scholar 

  25. Koh YS, Rountree N (2005) Finding sporadic rules using Apriori-inverse. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 97–106

    Chapter  Google Scholar 

  26. Leung CK, Khan QI, Li Z, Hoque T (2007) CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowl Inf Syst 11(3):287–311

    Article  Google Scholar 

  27. Li Y (2011) Data mining: concepts, background and methods of integrating uncertainty. In data mining

    Google Scholar 

  28. Lin CW, Hong TP, Lu WH (2011) An effective tree structure for mining high utility itemsets. Expert Syst Appl 38(6):7419–7424

    Article  Google Scholar 

  29. Lin CW, Hong TP, Lu WH (2009) The Pre-FUFP algorithm for incremental mining. Expert Syst Appl 36(5):9498–9505

    Article  Google Scholar 

  30. Lin JC, Gan W, Fournier-Viger P, Hong TP, Tseng VS (2016) Fast algorithms for mining high-utility itemsets with various discount strategies. Adv Eng Inform 30(2):109–126

    Article  Google Scholar 

  31. Lin JC, Gan W, Fournier-Viger P, Hong TP, Tseng VS (2015) Mining potential high-utility itemsets over uncertain databases. In: Proceedings of the ASE bigdata and social informatics 2015 ACM

    Google Scholar 

  32. Lin WY, Tseng MC (2006) Automated support specification for efficient mining of interesting association rules. J Inf Sci 32(3):238–250

    Article  Google Scholar 

  33. Lin YC, Wu CW, Tseng VS (2015) Mining high utility itemsets in big data. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Cham, pp 649–661

    Chapter  Google Scholar 

  34. Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining ACM, pp 337–341

    Google Scholar 

  35. Liu M, Qu J (2012) Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM international conference on information and knowledge management, ACM, pp 55–64, 29 Oct 2012

    Google Scholar 

  36. Liu Y, Liao WK, Choudhary A (2005) A fast high utility itemsets mining algorithm. In: Proceedings of the 1st international workshop on Utility-based data mining ACM, pp 90–99

    Google Scholar 

  37. Liu Y, Liao WK, Choudhary A (2005) A two-phase algorithm for fast discovery of high utility itemsets. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 689–695

    Chapter  Google Scholar 

  38. Mahgoub H, Rosner D (2006) Mining association rules from unstructured documents. In: Proceedings of 3rd international conference on knowledge mining, pp 167–172

    Google Scholar 

  39. Ninoria SZ, Thakur SS (2017) Study of high utility itemset mining. Int J Comput Appl 175(4):43–50

    Google Scholar 

  40. Patel AM, Bhalodiya D (2014) A survey on frequent itemset mining techniques using GPU. Int J Innov Res Technol 1(5)

    Google Scholar 

  41. PhridviRaj MS, GuruRao CV (2014) Data mining–past, present and future–a typical survey on data streams. Procedia Technol 12:255–263

    Article  Google Scholar 

  42. Pillai J, Vyas OP, Muyeba M (2013) Huri–a novel algorithm for mining high utility rare itemsets. In: Advances in computing and information technology. Springer, Berlin, Heidelberg, pp 531–540

    Chapter  Google Scholar 

  43. Pillai J, Vyas OP (2011) High utility rare item set mining (HURI): an approach for extracting high utility rare item sets. J Futur Eng Technol 7(1)

    Article  Google Scholar 

  44. Pillai J, Vyas OP (2010) Overview of itemset utility mining and its applications. Int J Comput Appl 5(11):9–13

    Google Scholar 

  45. Pillai J, Vyas OP (2013) Transaction profitability using HURI algorithm [tphuri]. Int J Bus Inf Syst 2(1)

    Google Scholar 

  46. Pillai J, Vyas OP (2011) User centric approach to itemset utility mining in market basket analysis. Int J Comput Sci Eng 3(1):393–400

    Google Scholar 

  47. Poovammal E, Ponnavaikko M (2009) Utility independent privacy preserving data mining on vertically partitioned data 1. Int J Comput Sci 5:666–673

    Google Scholar 

  48. Savasere A, Omiecinski ER, Navathe SB (1995) An efficient algorithm for mining association rules in large databases. Georgia Institute of Technology 502

    Google Scholar 

  49. Shankar S, Babu N, Purusothaman T, Jayanthi S (2009) A fast algorithm for mining high utility itemsets. In: Advance computing conference, IEEE international, pp 1459–1464

    Google Scholar 

  50. Shie BE, Tseng VS, Yu PS (2010) Online mining of temporal maximal utility itemsets from data streams. In: Proceedings of the 2010 ACM symposium on applied computing ACM, pp 1622 –1626

    Google Scholar 

  51. Shridhar M, Parmar M (2017) Survey on association rule mining and its approaches, pp 129–135

    Google Scholar 

  52. Szathmary L, Napoli A, Valtchev P (2007) Towards rare itemset mining. In: Tools with artificial intelligence 19th IEEE international conference, vol 1, pp 305–312

    Google Scholar 

  53. Tang P, Turkia MP (2006) Parallelizing frequent itemset mining with FP-trees. In: Computers and their applications, pp 30–35

    Google Scholar 

  54. Thakur SS, Ninoria SZ (2017) An improved progressive sampling based approach for association rule mining. Int J Comput Appl 165:7

    Google Scholar 

  55. Toivonen H (1996) Sampling large databases for association rules. VLDB, vol 96, pp 134–145

    Google Scholar 

  56. Tseng V, Wu CW, Fournier-Viger P, Philip SY (2016) Efficient algorithms for mining top-k high utility itemsets. IEEE Trans Knowl Data Eng (1)

    Google Scholar 

  57. 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 

  58. Yao H, Hamilton HJ, Geng L (2006) A unified framework for utility-based measures for mining itemsets. In: Proceedings of ACM SIGKDD 2nd Workshop on Utility-Based Data Mining, pp 28–37

    Google Scholar 

  59. Yun H, Ha D, Hwang B, Ryu KH (2003) Mining association rules on significant rare data using relative support. J Syst Softw 67(3):181–191

    Article  Google Scholar 

  60. Yun U, Leggett JJ (2005) WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Proceedings of the 2005 SIAM international conference on data mining, pp 636–640

    Google Scholar 

  61. 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 

  62. Yun U (2007) Efficient mining of weighted interesting patterns with a strong weight and/or support affinity. Inf Sci 177(17):477–99

    Article  MathSciNet  Google Scholar 

  63. Yun U (2009) On pushing weight constraints deeply into frequent itemset mining. Intell Data Anal 13(2):359–383

    Article  Google Scholar 

  64. Zida S, Fournier-Viger P, Lin JC, Wu CW, Tseng VS (2015) EFIM: a highly efficient algorithm for high—utility itemset mining. In: Mexican international conference on artificial intelligence. Springer, pp 530–546

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, RDVV, Jabalpur, Madhya Pradesh, India

    Shalini Zanzote Ninoria & S. S. Thakur

  2. Department of Applied Mathematics, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

    Shalini Zanzote Ninoria & S. S. Thakur

Authors
  1. Shalini Zanzote Ninoria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. S. Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shalini Zanzote Ninoria .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sagar Institute of Research and Technology, Bhopal, Madhya Pradesh, India

    Rajesh Kumar Shukla

  2. Department of Computer Science and Engineering, University Teaching Department, Rajiv Gandhi Technical University (State Technological University), Bhopal, Madhya Pradesh, India

    Jitendra Agrawal

  3. School of Information Technology, Rajiv Gandhi Technical University (State Technological University), Bhopal, Madhya Pradesh, India

    Sanjeev Sharma

  4. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Narendra S. Chaudhari

  5. Department of Computer Science and Engineering, Indian Institute of Technology BHU, Varanasi, Uttar Pradesh, India

    K. K. Shukla

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ninoria, S.Z., Thakur, S.S. (2020). Review on High Utility Rare Itemset Mining. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, vol 100. Springer, Singapore. https://doi.org/10.1007/978-981-15-2071-6_31

Download citation

Publish with us

Policies and ethics

Search

Navigation