Skip to main content
SpringerLink
Log in

A Survey of High Utility Itemset Mining

  • Chapter
  • First Online:
High-Utility Pattern Mining

Part of the book series: Studies in Big Data ((SBD,volume 51))

Abstract

High utility pattern mining is an emerging data science task, which consists of discovering patterns having a high importance in databases. The utility of a pattern can be measured in terms of various objective criterias such as its profit, frequency, and weight. Among the various kinds of high utility patterns that can be discovered in databases, high utility itemsets are the most studied. A high utility itemset is a set of values that appears in a database and has a high importance to the user, as measured by a utility function. High utility itemset mining generalizes the problem of frequent itemset mining by considering item quantities and weights. A popular application of high utility itemset mining is to discover all sets of items purchased together by customers that yield a high profit. This chapter provides an introduction to high utility itemset mining, reviews the state-of-the-art algorithms, their extensions, applications, and discusses research opportunities. This chapter is aimed both at those who are new to the field of high utility itemset mining, as well as researchers working in the field.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    The notation \(2^I\) denotes all itemsets that can be created using items from a set of items I. It is also called the powerset of I.

References

  1. Aggarwal, C.C.: Data Mining: The Textbook. Springer, Heidelberg (2015)

    MATH  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference Very Large Data Bases, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  3. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S.: A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J. 32(5), 676–686 (2010)

    Article  Google Scholar 

  4. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Choi, H.J.: A framework for mining interesting high utility patterns with a strong frequency affinity. Inf. Sci. 181(21), 4878–4894 (2011)

    Google Scholar 

  5. Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K.: Efficient tree structures for high-utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)

    Article  Google Scholar 

  6. Alkan, O.K., Karagoz, P.: Crom and huspext: improving efficiency of high utility sequential pattern extraction. IEEE Trans. Knowl. Data Eng. 27(10), 2645–2657 (2015)

    Article  Google Scholar 

  7. Bansal, R., Dawar, S., Goyal, V.: An efficient algorithm for mining high-utility itemsets with discount notion. In: Proceedings of the International Conference on Big Data Analytics, pp. 84–98. Springer (2015)

    Google Scholar 

  8. Barron, A., Rissanen, J., Yu, B.: The minimum description length principle in coding and modeling. IEEE Trans. Inf. Theory 44(6), 2743–2760 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  9. Bouasker, S., Ben Yahia, S.: Key correlation mining by simultaneous monotone and anti-monotone constraints checking. In: Proceedings of the 30th Symposium on Applied Computing, pp. 851–856. ACM (2015)

    Google Scholar 

  10. Brauckhoff, D., Dimitropoulos, X., Wagner, A., Salamatian, K.: Anomaly extraction in backbone networks using association rules. IEEE/ACM Trans. Netw. 20(6), 1788–1799 (2012)

    Article  Google Scholar 

  11. Chan, R., Yang, Q., Shen, Y.: Mining high utility itemsets. In: Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 19–26. IEEE (2003)

    Google Scholar 

  12. Chi, T.T., Fournier-Viger, P.: A survey of high utility sequential patten mining. In: Fournier-Viger et al. (eds). High-Utility Pattern. Mining: Theory, Algorithms and Applications. Springer (2018) (to appear)

    Google Scholar 

  13. Chu, C., Tseng, V.S., Liang, T.: An efficient algorithm for mining high utility itemsets with negative item values in large databases. Appl. Math. Comput. 215(2), 767–778 (2009)

    MATH  Google Scholar 

  14. Dam, T.-L., Li, K., Fournier-Viger, P., Duong, H.: CLS-Miner: Efficient and effective closed high utility itemset mining. Front. Comput. Sci. (2018). https://doi.org/10.1007/s11704-016-6245-4

  15. Dam, T.-L., Li, K., Fournier-Viger, P., Duong, H.: An efficient algorithm for mining top-k on-shelf high utility itemsets. Knowl. Inf. Syst. 52(2), 621–655 (2017)

    Article  Google Scholar 

  16. Duan, Y., Fu, X., Luo, B., Wang, Z., Shi, J., Du, X.: Detective: automatically identify and analyze malware processes in forensic scenarios via DLLs. In: Proceedings of the 2015 IEEE International Conference on Communications, pp. 5691–5696. IEEE (2015)

    Google Scholar 

  17. Duong, Q.H., Fournier-Viger, P., Ramampiaro, H., Norvag, K., Dam, T.-L.: Efficient high utility itemset mining using buffered utility-lists. Appl. Intell. 48(7), 1859–1877 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Duong, H., Ramampiaro, H., Norvag, K., Fournier-Viger, P., Dam, T.-L.: High utility drift detection in quantitative data streams. Knowl. Based Syst. 157(1), 34–51 (2018)

    Article  Google Scholar 

  20. Fernando, B., Elisa F., Tinne T.: Effective use of frequent itemset mining for image classification. In: Proceedings of the 12th European Conference on Computer Vision, pp. 214–227. Springer (2012)

    Google Scholar 

  21. Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3389–3393 (2014)

    MATH  Google Scholar 

  22. Fournier-Viger, P., Lin, J.C.-W., Dinh, T., Le, H.B.: Mining correlated high-utility itemsets using the bond measure. In: Proceedings of the International Conference Hybrid Artificial Intelligence Systems, pp. 53–65. Springer (2016)

    Google Scholar 

  23. Fournier-Viger, P., Lin, J.C.-W., Duong, Q.-H., Dam, T.-L.: FHM+: faster high-utility itemset mining using length upper-bound reduction. In: Proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 115–127. Springer (2016)

    Google Scholar 

  24. Fournier-Viger, P., Lin, J.C.-W., Duong, Q.H., Dam, T.L.: PHM: mining periodic high-utility itemsets. In: Proceedings of the16th Industrial Conference on Data Mining, pp. 64–79. Springer (2016)

    Google Scholar 

  25. Fournier-Viger, P., Lin, J.C.-W., Gomariz, A., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: Proceedings of the 19th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 36–40. Springer (2016)

    Google Scholar 

  26. Fournier-Viger, P., Lin, J.C.-W., Gueniche, T., Barhate, P.: Efficient incremental high utility itemset mining. In: Proceedings of the 5th ASE International Conference on Big Data. ASE (2015)

    Google Scholar 

  27. Fournier-Viger, P., Lin, J.C.-W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)

    Google Scholar 

  28. Fournier-Viger, P., Lin, J.C.-W., Vo, B, Chi, T.T., Zhang, J., Le, H. B.: A survey of itemset mining. WIREs Data Mining and Knowledge Discovery, pp. e1207 (2017). https://doi.org/10.1002/widm.1207

    Google Scholar 

  29. Fournier-Viger, P., Lin, C.W., Wu, C.-W., Tseng, V.S., Faghihi, U.: Mining minimal high-utility itemsets. In: Proceedings of the 27th International Conference on Database and Expert Systems Applications, pp. 88–101. Springer (2016)

    Google Scholar 

  30. Fournier-Viger, P., Wu, C.W., Tseng, V.S.: Novel concise representations of high utility itemsets using generator patterns. In: Proceedings of the 10th International Conference on Advanced Data Mining and Applications, pp. 30–43. Springer (2014)

    Google Scholar 

  31. Fournier-Viger, P., Wu, C.W., Zida, S., Tseng, V.S.: FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Proceedings of the 21st International Symposium Methodologies for Intelligent Systems, pp. 83–92. Springer (2014)

    Google Scholar 

  32. Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., Dinh, T., Le, B.: Mining correlated high-utility itemsets using various correlation measures. Logic J. IGPL (2018) (Oxford Academic, to appear)

    Google Scholar 

  33. Fournier-Viger, P., Zhang, Y., Lin, J.C.-W., Fujita, H., Koh, : Y.-S.: Mining local high utility itemsets. In: Proceedings of the 29th International Conference on Database and Expert Systems Applications (DEXA 2018). Springer (2018) (to appear)

    Google Scholar 

  34. Fournier-Viger, P., Zida, S.: FOSHU: faster on-shelf high utility itemset mining–with or without negative unit profit. In: Proceedings of the 30th Symposium on Applied Computing, pp. 857–864. ACM (2015)

    Google Scholar 

  35. Fournier-Viger, P., Zida, S., Lin, C.W., Wu, C.-W., Tseng, V.S.: EFIM-closed: fast and memory efficient discovery of closed high-utility itemsets. In: Proceedings of the 12th International Conference on Machine Learning and Data Mining, pp. 199–213. Springer (2016)

    Google Scholar 

  36. Gan, W., Lin, J.C.-W., Fournier-Viger, P., Chao, H.C.: More efficient algorithms for mining high-utility itemsets with multiple minimum utility thresholds. In: Proceedings of the 26th International Conference on Database and Expert Systems Applications, pp. 71–87. Springer (2016)

    Google Scholar 

  37. Glatz, E., Mavromatidis, S., Ager, B., Dimitropoulos, X.: Visualizing big network traffic data using frequent pattern mining and hypergraphs. Computing 96(1), 27–38 (2014)

    Article  Google Scholar 

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

    Chapter  MATH  Google Scholar 

  39. Han, J., Pei, J., Ying, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  40. Hegland, M.: The apriori algorithm–a tutorial. In: Mathematics and Computation in Imaging Science and Information Processing, vol. 11, pp. 209–62 (2005)

    Google Scholar 

  41. Hong, T.P., Lee, C.H., Wang, S.L.: Mining high average-utility itemsets. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 2526-2530. IEEE (2009)

    Google Scholar 

  42. Krishnamoorthy, S.: Efficient mining of high utility itemsets with multiple minimum utility thresholds. Eng. Appl. Artif. Intell. 69, 112–126 (2018)

    Article  Google Scholar 

  43. Lan, G.-C., Hong, T.-P., Huang, J.-P., Tseng, V.S.: On-shelf utility mining with negative item values. Expert Syst. Appl. 41, 3450–3459 (2014)

    Article  Google Scholar 

  44. Lan, G.-C., Hong, T.-P., Tseng, V.S.: Discovery of high utility itemsets from on-shelf time periods of products. Expert Syst. Appl. 38, 5851–5857 (2011)

    Article  Google Scholar 

  45. Lan, G.-C., Hong, T.P., Tseng, V.S.: A projection-based approach for discovering high average-utility itemsets. J. Inf. Sci. Eng. 28(1), 193–209 (2012)

    Google Scholar 

  46. Lan, G.-C., Hong, T.-P., Tseng, V.S.: Efficiently mining high average-utility itemsets with an improved upper-bound strategy. Int. J. Inf. Technol. Decis. Mak. 11(5), 1009–1030 (2012)

    Article  Google Scholar 

  47. Lan, G.-C., Hong, T.P., Tseng, V.S.: An efficient projection-based indexing approach for mining high utility itemsets. Knowl. Inf. Syst. 38(1), 85–107 (2014)

    Article  Google Scholar 

  48. Lin, J.C.-W., Fournier-Viger, P., Gan, W.: FHN: an efficient algorithm for mining high-utility itemsets with negative unit profits. Knowl. Based Syst. 111(1), 283–298 (2016)

    Article  Google Scholar 

  49. Lin, J.C.-W., Gan, W., Fournier-Viger, P., Chen, H.-C.: Mining recent high-utility patterns from temporal databases with time-sensitive constraint. In: Proceedings of the 18th International Conference on Data Warehousing and Knowledge Discovery, pp. 3–16. Springer (2016)

    Google Scholar 

  50. Lin, J.C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Chao, H.-C.: FDHUP: fast algorithm for mining discriminative high utility patterns. Knowl. Inf. Syst. 51(3), 873–909 (2016)

    Article  Google Scholar 

  51. Lin, J.C.-W., Gan, W., Fournier-Viger, P., Hong, T.P., Tseng, V.S.: Fast algorithms for mining high-utility itemsets with various discount strategies. Adv. Eng. Inf. 30(2), 109–126 (2016)

    Article  Google Scholar 

  52. Lin, J.C.-W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–24 (2011)

    Article  Google Scholar 

  53. Lin, J.C.-W., Li, T., Fournier-Viger, P., Hong, T.-P., Voznak, M., Zhan, J.: An efficient algorithm to mine high average-utility itemsets. Adv. Eng. Inf. 30(2), 233–243 (2016)

    Article  Google Scholar 

  54. Lin, J.C.-W., Liu, Q., Fournier-Viger, P., Hong, T.-P., Voznak, M., Zhan, J.: A sanitization approach for hiding sensitive itemsets based on particle swarm optimization. Eng. Appl. Artif. Intell. 53, 1–18 (2016)

    Article  Google Scholar 

  55. Lin, J.C.-W., Ren, S., Fournier-Viger, P., Hong, T.-P.: EHAUPM: efficient high average-utility pattern mining with tighter upper-bounds. In: IEEE Access 5, 12927–12940 (2017)

    Article  Google Scholar 

  56. Lin, Y.C., Wu, C.W., Tseng, V.S.: Mining high utility itemsets in big data. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 649–661. Springer (2015)

    Google Scholar 

  57. Lin., J.C.-W., Yang, L., Fournier-Viger, Frnda, J., Sevcik, L., Voznak, M.: An evolutionary algorithm to mine high-utility itemsets. Adv. Electr. Electron. Eng. 13(5), 392–398 (2015)

    Google Scholar 

  58. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM International Conference Information and knowledge management, pp. 55–64. ACM (2012)

    Google Scholar 

  59. Liu, Y., Liao, W.K., Choudhary, A.N.: A two-phase algorithm for fast discovery of high utility itemsets. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 689–695. Springer (2005)

    Google Scholar 

  60. Liu, J., Wang, K., Fung, B.: Direct discovery of high utility itemsets without candidate generation. In: Proceedings of the 12th IEEE International Conference Data Mining, pp. 984–989. IEEE (2012)

    Google Scholar 

  61. Liu, Y., Zhao, Y., Chen, L., Pei, J., Han, J.: Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Trans. Parallel Distrib. Syst. 23(11), 2138–2149 (2012)

    Article  Google Scholar 

  62. Lu, T., Vo, B., Nguyen, H.T., Hong, T.P.: A new method for mining high average utility itemsets. In: Proceedings of the 13th International Conference on Computer Information Systems and Industrial Management Applications, pp. 33–42. Springer (2014)

    Google Scholar 

  63. Lucchese, C., Orlando, S., Perego, R.: Fast and memory efficient mining of frequent closed itemsets. IEEE Trans. Knowl. Data Eng. 18(1), 21–36 (2006)

    Article  Google Scholar 

  64. Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200. ACM (2012)

    Google Scholar 

  65. Mwamikazi, E., Fournier-Viger, P., Moghrabi, C., Baudouin, R.: A dynamic questionnaire to further reduce questions in learning style assessment. In: Proceedings of the 10th International Conference Artificial Intelligence Applications and Innovations, pp. 224–235. Springer (2014)

    Google Scholar 

  66. Naulaerts, S., Meysman, P., Bittremieux, W., Vu, T.N., Berghe, W.V., Goethals, B., Laukens, K.: A primer to frequent itemset mining for bioinformatics. Brief. Bioinform. 16(2), 216–231 (2015)

    Article  Google Scholar 

  67. Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)

    Article  MathSciNet  Google Scholar 

  68. Pei, J., Han, J.: Constrained frequent pattern mining: a pattern-growth view. ACM SIGKDD Explor. Newsl. 4(1), 31–39 (2012)

    Article  Google Scholar 

  69. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings of the 2001 IEEE International Conference Data Mining, pp. 441–448. IEEE (2001)

    Google Scholar 

  70. Peng, A.X., Koh, Y.S., Riddle, P.: mHUIMiner: a fast high utility itemset mining algorithm for sparse datasets. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 196–207 (2017)

    Chapter  Google Scholar 

  71. Qu, J.-F., Liu, M., Fournier-Viger, P.: Efficient algorithms for high utility itemset mining without candidate generation. In: Fournier-Viger et al. (eds). High-Utility Pattern. Mining: Theory, Algorithms and Applications. Springer (2018) (to appear)

    Google Scholar 

  72. Ryang, H., Yun, : U.: Top-k high utility pattern mining with effective threshold raising strategies. Knowl. Based Syst. 76, 109–126 (2015)

    Article  Google Scholar 

  73. Ryang, H., Yun, U.: High utility pattern mining over data streams with sliding window technique. Expert Syst. Appl. 57, 214–231 (2016)

    Article  Google Scholar 

  74. Ryang, H., Yun, U., Ryu, K.: Discovering high utility itemsets with multiple minimum supports. Intell. Data Anal. 18(6), 1027–1047 (2014)

    Article  Google Scholar 

  75. Shie, B.-E., Yu, P.S., Tseng, V.S.: Efficient algorithms for mining maximal high utility itemsets from data streams with different models. Expert Syst. Appl. 39(17), 12947–12960 (2012)

    Article  Google Scholar 

  76. Song, W., Huang, C.: Discovering high utility itemsets based on the artificial bee colony algorithm. In: Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 3–14. Springer (2018)

    Google Scholar 

  77. Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Discovering periodic-frequent patterns in transactional databases. In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 242–253. Springer (2009)

    Google Scholar 

  78. Truong, T., Duong, H., Le, B., Fournier-Viger, P.: Efficient vertical mining of high average-utility itemsets based on novel upper-bounds. IEEE Trans. Knowl. Data Eng. (2018). https://doi.org/10.1109/TKDE.2018.2833478

    Article  Google Scholar 

  79. Tseng, V.S., Shie, B.-E., Wu, C.-W., Yu., P.S.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)

    Article  Google Scholar 

  80. Tseng, V., Wu, C., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining top-k high utility itemsets. IEEE Trans. Knowl. Data Eng. 28(1), 54–67 (2016)

    Article  Google Scholar 

  81. Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the ICDM’04 Workshop on Frequent Itemset Mining Implementations. CEUR (2004)

    Google Scholar 

  82. Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626 (2006)

    Article  Google Scholar 

  83. Yao, H., Hamilton, H.J., Geng, L.: A unified framework for utility-based measures for mining itemsets. In: Proceeidngs of the ACM SIGKDD Workshop on Utility-Based Data Mining, pp. 28–37. ACM (2006)

    Google Scholar 

  84. Yin, J., Zheng, Z., Cao, L.: USpan: an efficient algorithm for mining high utility sequential patterns. In: Proceedings of the 18th, pp. 660–668. ACM (2012)

    Google Scholar 

  85. Yun, U., Kim, D.: Mining of high average-utility itemsets using novel list structure and pruning strategy. Future Gener. Comput. Syst. 68, 346–360 (2016)

    Article  Google Scholar 

  86. Yun, U., Ryang, H.: Incremental high utility pattern mining with static and dynamic databases. Appl. Intell. 42(2), 323–352 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  88. Wu, C.W., Fournier-Viger, P., Gu, J.-Y., Tseng, V.S.: Mining closed+ high utility itemsets without candidate generation. In: Proceedings of the 2015 Conference on Technologies and Applications of Artificial Intelligence, pp. 187–194. IEEE (2015)

    Google Scholar 

  89. Wu, C.-W., Fournier-Viger, P., Gu, J.-Y., Tseng, V.-S.: Efficient algorithms for high utility itemset mining without candidate generation. In: Fournier-Viger et al. (eds). High-Utility Pattern. Mining: Theory, Algorithms and Applications. Springer (2018) (to appear)

    Google Scholar 

  90. Wu, C.-W., Fournier-Viger, P., Yu., P.S., Tseng, V.S.: Efficient mining of a concise and lossless representation of high utility itemsets. In: Proceedings of the 11th IEEE International Conference on Data Mining, pp. 824–833. IEEE (2011)

    Google Scholar 

  91. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  Google Scholar 

  92. Zhang, L., Fu, G., Cheng, F., Qiu, J., Su, Y.: A multi-objective evolutionary approach for mining frequent and high utility itemsets. Appl. Soft Comput. 62, 974–986 (2018)

    Article  Google Scholar 

  93. Zida, S., Fournier-Viger, P., Wu, C.-W., Lin, J.C.-W., Tseng, V.S.: Efficient mining of high utility sequential rules. In: Proceedings of the 11th International Conference on Machine Learning and Data Mining, pp. 157–171. Springer (2015)

    Google Scholar 

  94. Zida, S., Fournier-Viger, P., Lin, J.C.-W., Wu, C.W., Tseng, V.S.: EFIM: a highly efficient algorithm for high-utility itemset mining. In: Proceedings of the 14th Mexican International Conference Artificial Intelligence, pp. 530–546. Springer (2015)

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Science Fundation of China and Harbin Institute of Technology.

Author information

Authors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Department of Computing Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

  3. University of Dalat, Dalat, Vietnam

    Tin Truong-Chi

  4. University of Quebec, Montreal, Canada

    Roger Nkambou

Authors
  1. Philippe Fournier-Viger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerry Chun-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tin Truong-Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roger Nkambou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philippe Fournier-Viger .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Université du Québec à Montréal, Montreal, QC, Canada

    Roger Nkambou

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R. (2019). A Survey of High Utility Itemset Mining. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_1

Download citation

Publish with us

Policies and ethics

Search

Navigation