Skip to main content
SpringerLink
Log in

TKE: Mining Top-K Frequent Episodes

  • Conference paper
  • First Online:
Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (IEA/AIE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12144))

Abstract

Frequent episode mining is a popular data mining task for analyzing a sequence of events. It consists of identifying all subsequences of events that appear at least minsup times. Though traditional episode mining algorithms have many applications, a major problem is that setting the minsup parameter is not intuitive. If set too low, algorithms can have long execution times and find too many episodes, while if set too high, algorithms may find few patterns, and hence miss important information. Choosing minsup to find enough but not too many episodes is typically done by trial and error, which is time-consuming. As a solution, this paper redefines the task of frequent episode mining as top-k frequent episode mining, where the user can directly set the number of episodes k to be found. A fast algorithm named TKE is presented to find the top-k episodes in an event sequence. Experiments on benchmark datasets shows that TKE performs well and that it is a valuable alternative to traditional frequent episode mining algorithms.

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 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

References

  1. Achar, A., Laxman, S., Sastry, P.S.: A unified view of the apriori-based algorithms for frequent episode discovery. Knowl. Inf. Syst. 31(2), 223–250 (2012). https://doi.org/10.1007/s10115-011-0408-2

    Article  Google Scholar 

  2. Amiri, M., Mohammad-Khanli, L., Mirandola, R.: An online learning model based on episode mining for workload prediction in cloud. Future Gener. Comput. Syst. 87, 83–101 (2018)

    Article  Google Scholar 

  3. Ao, X., Luo, P., Li, C., Zhuang, F., He, Q.: Online frequent episode mining. In: Proceedings 31st IEEE International Conference on Data Engineering, pp. 891–902 (2015)

    Google Scholar 

  4. Ao, X., Luo, P., Wang, J., Zhuang, F., He, Q.: Mining precise-positioning episode rules from event sequences. IEEE Trans. Knowl. Data Eng. 30(3), 530–543 (2018)

    Article  Google Scholar 

  5. Ao, X., Shi, H., Wang, J., Zuo, L., Li, H., He, Q.: Large-scale frequent episode mining from complex event sequences with hierarchies. ACM Trans. Intell. Syst. Technol. (TIST) 10(4), 1–26 (2019)

    Article  Google Scholar 

  6. Cheng, Z., Flouvat, F., Selmaoui-Folcher, N.: Mining recurrent patterns in a dynamic attributed graph. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 631–643. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57529-2_49

    Chapter  Google Scholar 

  7. Fahed, L., Brun, A., Boyer, A.: DEER: distant and essential episode rules for early prediction. Expert Syst. Appl. 93, 283–298 (2018)

    Article  Google Scholar 

  8. Fournier-Viger, P., Cheng, C., Lin, J.C.-W., Yun, U., Kiran, R.U.: TKG: efficient mining of top-K frequent subgraphs. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P.K. (eds.) BDA 2019. LNCS, vol. 11932, pp. 209–226. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37188-3_13

    Chapter  Google Scholar 

  9. Fournier-Viger, P., Li, J., Lin, J.C.W., Chi, T.T., Kiran, R.U.: Mining cost-effective patterns in event logs. Knowl. Based Syst. 191, 105241 (2020)

    Article  Google Scholar 

  10. Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8

    Chapter  Google Scholar 

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

    Google Scholar 

  12. Fournier-Viger, P., Yang, P., Lin, J.C.-W., Yun, U.: HUE-span: fast high utility episode mining. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds.) ADMA 2019. LNCS (LNAI), vol. 11888, pp. 169–184. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35231-8_12

    Chapter  Google Scholar 

  13. Fournier-Viger, P., Zhang, Y., Lin, J.C.W., Fujita, H., Koh, Y.S.: Mining local and peak high utility itemsets. Inf. Sci. 481, 344–367 (2019)

    Article  MathSciNet  Google Scholar 

  14. Helmi, S., Banaei-Kashani, F.: Mining frequent episodes from multivariate spatiotemporal event sequences. In: Proceedings 7th ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 1–8 (2016)

    Google Scholar 

  15. Huang, K., Chang, C.: Efficient mining of frequent episodes from complex sequences. Inf. Syst. 33(1), 96–114 (2008)

    Article  Google Scholar 

  16. Iwanuma, K., Takano, Y., Nabeshima, H.: On anti-monotone frequency measures for extracting sequential patterns from a single very-long data sequence. In: Proceedings IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 213–217 (2004)

    Google Scholar 

  17. Venkatesh, J.N., Uday Kiran, R., Krishna Reddy, P., Kitsuregawa, M.: Discovering periodic-correlated patterns in temporal databases. In: Hameurlain, A., Wagner, R., Hartmann, S., Ma, H. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII. LNCS, vol. 11250, pp. 146–172. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-58384-5_6

    Chapter  Google Scholar 

  18. Li, L., Li, X., Lu, Z., Lloret, J., Song, H.: Sequential behavior pattern discovery with frequent episode mining and wireless sensor network. IEEE Commun. Mag. 55(6), 205–211 (2017)

    Article  Google Scholar 

  19. Liao, G., Yang, X., Xie, S., Yu, P.S., Wan, C.: Mining weighted frequent closed episodes over multiple sequences. Tehnički vjesnik 25(2), 510–518 (2018)

    Google Scholar 

  20. Lin, S., Qiao, J., Wang, Y.: Frequent episode mining within the latest time windows over event streams. Appl. Intell. 40(1), 13–28 (2013). https://doi.org/10.1007/s10489-013-0442-8

    Article  Google Scholar 

  21. Lin, Y., Huang, C., Tseng, V.S.: A novel methodology for stock investment using high utility episode mining and genetic algorithm. Appl. Soft Comput. 59, 303–315 (2017)

    Article  Google Scholar 

  22. Luna, J.M., Fournier-Viger, P., Ventura, S.: Frequent itemset mining: a 25 years review. In: Lepping, J., (ed.) Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 6, p. e1329. Wiley, Hoboken (2019)

    Google Scholar 

  23. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proceedings 1st International Conference on Knowledge Discovery and Data Mining (1995)

    Google Scholar 

  24. Patnaik, D., Laxman, S., Chandramouli, B., Ramakrishnan, N.: Efficient episode mining of dynamic event streams. In: 2012 IEEE 12th International Conference on Data Mining, pp. 605–614 (2012)

    Google Scholar 

  25. Rathore, S., Dawar, S., Goyal, V., Patel, D.: Top-k high utility episode mining from a complex event sequence. In: Proceedings of the 21st International Conference on Management of Data, Computer Society of India (2016)

    Google Scholar 

  26. Su, M.Y.: Applying episode mining and pruning to identify malicious online attacks. Comput. Electr. Eng. 59, 180–188 (2017)

    Article  Google Scholar 

  27. Truong, T., Duong, H., Le, B., Fournier-Viger, P.: Fmaxclohusm: an efficient algorithm for mining frequent closed and maximal high utility sequences. Eng. Appl. Artif. Intell. 85, 1–20 (2019)

    Article  Google Scholar 

  28. Truong, T., Duong, H., Le, B., Fournier-Viger, P., Yun, U.: Efficient high average-utility itemset mining using novel vertical weak upper-bounds. Knowl. Based Syst. 183, 104847 (2019)

    Article  Google Scholar 

  29. Wenzhe, L., Qian, W., Luqun, Y., Jiadong, R., Davis, D.N., Changzhen, H.: Mining frequent intra-sequence and inter-sequence patterns using bitmap with a maximal span. In: Proceedings 14th Web Information System and Applications Conference, pp. 56–61. IEEE (2017)

    Google Scholar 

  30. Wu, C., Lin, Y., Yu, P.S., Tseng, V.S.: Mining high utility episodes in complex event sequences. In: Proceedings 19th ACM SIGKDD International Conference on Knowledge Discovery, pp. 536–544 (2013)

    Google Scholar 

  31. Zhou, W., Liu, H., Cheng, H.: Mining closed episodes from event sequences efficiently. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6118, pp. 310–318. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13657-3_34

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Natural Sciences and Humanities, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. School of Computer Sciences and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Yanjun Yang & Peng Yang

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

    Jerry Chun-Wei Lin

  4. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun

Authors
  1. Philippe Fournier-Viger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanjun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jerry Chun-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Unil Yun
    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. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

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

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Iwate Prefectural University, Takizawa, Japan

    Jun Sasaki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fournier-Viger, P., Yang, Y., Yang, P., Lin, J.CW., Yun, U. (2020). TKE: Mining Top-K Frequent Episodes. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55789-8_71

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55788-1

  • Online ISBN: 978-3-030-55789-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation