• Simons J, Boverhof B, Aarts E and Zhan P. (2024). The influence of observation sequence features on the performance of the Bayesian hidden Markov model: A Monte Carlo simulation study. PLOS ONE. 10.1371/journal.pone.0314444. 19:12. (e0314444).

    https://dx.plos.org/10.1371/journal.pone.0314444

  • Alibasa M, Calvo R and Yacef K. (2022). Predicting Mood from Digital Footprints Using Frequent Sequential Context Patterns Features. International Journal of Human–Computer Interaction. 10.1080/10447318.2022.2073321. 39:10. (2061-2075). Online publication date: 15-Jun-2023.

    https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2073321

  • Robinson W and Aria A. (2018). Sequential fraud detection for prepaid cards using hidden Markov model divergence. Expert Systems with Applications. 10.1016/j.eswa.2017.08.043. 91. (235-251). Online publication date: 1-Jan-2018.

    https://linkinghub.elsevier.com/retrieve/pii/S0957417417305894

  • Moyse G, Lesot M and Bouchon-Meunier B. (2013). Linguistic summaries for periodicity detection based on mathematical morphology 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI). 10.1109/FOCI.2013.6602462. 978-1-4673-5901-6. (106-113).

    http://ieeexplore.ieee.org/document/6602462/

  • Liu Y, Zhao Y, Chen L, Pei J and Han J. (2012). Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays. IEEE Transactions on Parallel and Distributed Systems. 23:11. (2138-2149). Online publication date: 1-Nov-2012.

    https://doi.org/10.1109/TPDS.2011.307

  • Robinson W, Syed A, Akhlaghi A and Deng T. Pattern Discovery of User Interface Sequencing by Rehabilitation Clients with Cognitive Impairments. Proceedings of the 2012 45th Hawaii International Conference on System Sciences. (3001-3010).

    https://doi.org/10.1109/HICSS.2012.467

  • Jiang Y, Perng C and Li T. Natural event summarization. Proceedings of the 20th ACM international conference on Information and knowledge management. (765-774).

    https://doi.org/10.1145/2063576.2063688

  • Hadzic F and Hecker M. Alternative Approach to Tree-Structured Web Log Representation and Mining. Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01. (235-242).

    https://doi.org/10.1109/WI-IAT.2011.156

  • Weiβ C. (2011). Rule generation for categorical time series with Markov assumptions. Statistics and Computing. 21:1. (1-16). Online publication date: 1-Jan-2011.

    https://doi.org/10.1007/s11222-009-9141-z

  • Kiernan J and Terzi E. (2009). Constructing comprehensive summaries of large event sequences. ACM Transactions on Knowledge Discovery from Data. 3:4. (1-31). Online publication date: 1-Nov-2009.

    https://doi.org/10.1145/1631162.1631169

  • Kiernan J and Terzi E. EventSummarizer. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology. (1136-1139).

    https://doi.org/10.1145/1516360.1516497

  • Kiernan J and Terzi E. Constructing comprehensive summaries of large event sequences. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. (417-425).

    https://doi.org/10.1145/1401890.1401943

  • Jamasebi R, Johnson N, Kaffashi F, Redline S and Loparo K. (2008). A watermarking algorithm for polysomnography data 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 10.1109/IEMBS.2008.4650513. 978-1-4244-1814-5. (5720-5723).

    http://ieeexplore.ieee.org/document/4650513/

  • Liu Y, Chen L, Pei J, Chen Q and Zhao Y. Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays. Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications. (37-46).

    https://doi.org/10.1109/PERCOM.2007.23

  • Shirahama K, Ideno K and Uehara K. A Time-Constrained Sequential Pattern Mining for Extracting Semantic Events in Videos. Multimedia Data Mining and Knowledge Discovery. 10.1007/978-1-84628-799-2_20. (404-426).

    http://link.springer.com/10.1007/978-1-84628-799-2_20

  • Li C and Yoo J. Modeling student online learning using clustering. Proceedings of the 44th annual ACM Southeast Conference. (186-191).

    https://doi.org/10.1145/1185448.1185490

  • Shirahama K, Ideno K and Uehara K. (2006). Extracting Semantic Patterns in Video Using Time-constrained Sequential Pattern Mining. The Journal of The Institute of Image Information and Television Engineers. 10.3169/itej.60.1473. 60:9. (1473-1482).

    http://joi.jlc.jst.go.jp/JST.JSTAGE/itej/60.1473?from=CrossRef

  • Laxman S, Sastry P and Unnikrishnan K. (2005). Discovering Frequent Episodes and Learning Hidden Markov Models. IEEE Transactions on Knowledge and Data Engineering. 17:11. (1505-1517). Online publication date: 1-Nov-2005.

    https://doi.org/10.1109/TKDE.2005.181

  • Li C and Yoo J. A study of the effects of bias in criterion functions for temporal data clustering. Proceedings of the 43rd annual ACM Southeast Conference - Volume 1. (85-89).

    https://doi.org/10.1145/1167350.1167384

  • Related Work. Mining Sequential Patterns from Large Data Sets. 10.1007/0-387-24247-3_2. (5-12).

    http://link.springer.com/10.1007/0-387-24247-3_2

  • Jiao L, Wu Y, Wu G, Chang E and Wang Y. (2004). Anatomy of a multicamera video surveillance system. Multimedia Systems. 10:2. (144-163). Online publication date: 1-Aug-2004.

    https://doi.org/10.1007/s00530-004-0147-2

  • Wu G, Wu Y, Jiao L, Wang Y and Chang E. Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance. Proceedings of the eleventh ACM international conference on Multimedia. (528-538).

    https://doi.org/10.1145/957013.957126

  • Bolton R and Adams N. An iterative hypothesis-testing strategy for pattern discovery. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. (49-58).

    https://doi.org/10.1145/956750.956760

  • Hand D. (2002). Pattern Detection and Discovery. Pattern Detection and Discovery. 10.1007/3-540-45728-3_1. (1-12).

    http://link.springer.com/10.1007/3-540-45728-3_1