113
Views
0
CrossRef citations to date
0
Altmetric
Computers and Computing

Natural Exponent Inertia Weight based Particle Swarm Optimization for Mining Serial Episode Rules from Event Sequences

ORCID Icon & ORCID Icon

References

  • H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovery of frequent episodes in event sequences,” Data Mining Knowl. Disc., Vol. 1, no. 3, pp. 259–289, 1997. DOI: 10.1023/A:1009748302351
  • C. W. Wu, Y. F. Lin, P. S. Yu, and V. S. Tseng, “Mining high utility episodes in complex event sequences,” in Proceedings 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2013, pp. 536–544.
  • W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, V. S. Tseng, and S. Y. Philip, “A survey of utility-oriented pattern mining,” IEEE Trans. Knowl. Data Eng., Vol. 33, no. 4, pp. 1306–1327, 2019. DOI: 10.1109/TKDE.2019.2942594
  • A. Ng, and A. W. C. Fu, “Mining frequent episodes for relating financial events and stock trends,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Apr. 2003, pp. 27–39.
  • D. Patnaik, P. Butler, N. Ramakrishnan, N. L. Parida, B. J. Keller, and D. A. Hanauer, “Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges,” in Proceedings of the 17th ACM SIGKDD Conference Knowledge Discovery and Data Mining, Aug. 2011, pp. 360–368.
  • S. Laxman, P. Sastry, and K. Unnikrishnan, “Discovering frequent generalized episodes when events persist for different durations,” IEEE Trans. Knowl. Data Eng., Vol. 19, no. 9, pp. 1188–1201, 2007. DOI: 10.1109/TKDE.2007.1055
  • X. Ao, H. Shi, J. Wang, L. Zuo, H. Li, and Q. He, “Large-scale frequent episode mining from complex event sequences with hierarchies,” ACM Trans. Intell. Syst. Technol., Vol. 10, no. 4, pp. 1–26, 2019. DOI: 10.1145/3326163
  • Y. F. Lin, C. W. Wu, C. F. Huang, and V. S. Tseng, “Discovering utility-based episode rules in complex event sequences,” Expert Syst. Appl., Vol. 42, no. 12, pp. 5303–5314, 2015. DOI: 10.1016/j.eswa.2015.02.022
  • K. Y. Huang, and C. H. Chang, “Efficient mining of frequent episodes from complex sequences,” Inf. Syst., Vol. 33, no. 1, pp. 96–114, 2008. DOI: 10.1016/j.is.2007.07.003
  • N. Tatti, and B. Cule, “Mining closed episodes with simultaneous events,” in Proc. 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2011, pp. 1172–1180.
  • N. Tatti, and B. Cule, “Mining closed strict episodes,” Data Mining Knowl. Disc., Vol. 25, no. 1, pp. 34–66, 2012. DOI: 10.1007/s10618-011-0232-z
  • X. Ao, P. Luo, C. Li, F. Zhuang, Q. He, and Z. Shi, “Discovering and learning sensational episodes of news events,” in Proceedings of the 23rd Conference World Wide Web, Apr. 2014, pp. 217–218.
  • A. Achar, S. Laxman, R. Viswanathan, and P. S. Sastry, “Discovering injective episodes with general partial orders,” Data Mining Knowl. Disc., Vol. 25, no. 1, pp. 67–108, 2012. DOI: 10.1007/s10618-011-0233-y
  • J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P. S. Yu, “Discovering frequent closed partial orders from strings,” IEEE Trans. Knowl. Data Eng., Vol. 18, no. 11, pp. 1467–1481, 2006. DOI: 10.1109/TKDE.2006.172
  • X. Ma, H. Pang, and K. L. Tan, “Finding constrained frequent episodes using minimal occurrences,” in IEEE 4th Conference on Data Mining, Nov. 2004, pp. 471–474.
  • W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, and P. S. Yu, “A survey of parallel sequential pattern mining,” ACM Trans. Knowl. Disc. Data (TKDD), Vol. 13, no. 3, pp. 1–34, 2019. DOI: 10.1145/3314107
  • X. Ao, P. Luo, J. Wang, F. Zhuang, and Q. He, “Mining precise-positioning episode rules from event sequences,” IEEE Trans. Knowl. Data Eng., Vol. 30, no. 3, pp. 530–543, 2017. DOI: 10.1109/TKDE.2017.2773493
  • Y. Shi, and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of IEEE Conference on Evolutionary Computation, May 1998, pp. 69–73.
  • A. Ibrahim, S. Sastry, and P. S. Sastry, “Discovering compressing serial episodes from event sequences,” Knowl. Inf. Syst., Vol. 47, no. 2, pp. 405–432, 2016. DOI: 10.1007/s10115-015-0854-3
  • A. Achar, S. Laxman, and P. S. Sastry, “A unified view of the apriori-based algorithms for frequent episode discovery,” Knowl. Inf. Syst., Vol. 31, no. 2, pp. 223–250, 2012. DOI: 10.1007/s10115-011-0408-2
  • A. Achar, A. Ibrahim, and P. S. Sastry, “Pattern-growth based frequent serial episode discovery,” Data Knowl. Eng., Vol. 87, pp. 91–108, 2013. DOI: 10.1016/j.datak.2013.06.005
  • P. Fournier-Viger, Y. Yang, P. Yang, J. C. W. Lin, and U. Yun, “TKE: mining top-k frequent episodes,” in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Sep. 2020, pp. 832–845.
  • D. Patnaik, S. Laxman, B. Chandramouli, and N. Ramakrishnan, “Efficient episode mining of dynamic event streams,” in IEEE 12th Conference on Data Mining, Dec. 2012, pp. 605–614.
  • X. Ao, P. Luo, C. Li, F. Zhuang, and Q. He, “Online frequent episode mining,” in IEEE 31st Conference on Data Engineering, Apr. 2015, pp. 891–902.
  • T. You, Y. Li, B. Sun, and C. Du, “Multi-Source data stream Online frequent episode mining,” IEEE Access, Vol. 8, pp. 107465–107478, 2020. DOI: 10.1109/ACCESS.2020.2997337
  • H. K. Dai, “Episode-Rule mining with minimal occurrences via first local maximization in confidence,” in Proceedings of the 9th International Symposium on Information and Communication Technology, Dec. 2018, pp. 130–136.
  • L. Fahed, A. Brun, and A. Boyer, “DEER: Distant and Essential Episode Rules for early prediction,” Expert Syst. Appl., Vol. 93, pp. 283–298, 2018. DOI: 10.1016/j.eswa.2017.10.035
  • K. Poongodi, and A. S. Manzoor, “Mining entropy optimized parameter based precise positioning episode rules from event sequences,” in 11th International Conference on Advanced Computing, Dec. 2019, pp. 225–231.
  • P. Fournier-Viger, Y. Chen, F. Nouioua, and J. C. W. Lin, “Mining Partially-Ordered episode rules in an event sequence,” in Asian Conference on Intelligent Information and Database Systems, 2021, pp. 3–15.
  • P. Fournier-Viger, P. Yang, J. C. W. Lin, and U. Yun, “Hue-span: fast high utility episode mining,” in International Conference on Advanced Data Mining and Applications, Nov. 2019, pp. 169–184.
  • W. Gan, J. C. W. Lin, H. C. Chao, and P. S. Yu. Discovering High Utility Episodes in Sequences, arXiv preprint arXiv:1912.11670, 2019.
  • S. Wan, J. Chen, W. Gan, G. Chen, and V. Goyal. THUE: Discovering Top-K High Utility Episodes, arXiv preprint arXiv:2106.14830, 2021.
  • W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, and P. S. Yu, “Beyond frequency: utility mining with varied item-specific minimum utility,” ACM Trans. Internet Technol., Vol. 21, no. 1, pp. 1–32, 2021.
  • H. Mannila, and H. Toivonen, “Discovering generalized episodes using minimal occurrences,” Proc. KDD, Vol. 96, pp. 146–151, Aug. 1996.
  • https://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.