Abstract
Sequence mining is one of the most investigated tasks in data mining and it has been studied under several perspectives. With the rise of Big Data technologies, the perspective of efficiency becomes prominent especially when mining massive sequences. In this paper, we perform a thorough experimental evaluation of several algorithms for sequential pattern mining and we provide an analysis of the results focusing on the different algorithmic choices and how these affect the performance of each algorithm. Experiments performed on real-world and synthetic datasets highlight relevant differences between existing algorithms and provide indications for Big Data scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, New York, NY, USA, pp. 429–435. ACM (2002)
Cheng, Y., Lin, Y., Chiang, K., Tseng, V.S.: Mining sequential risk patterns from large-scale clinical databases for early assessment of chronic diseases: a case study on chronic obstructive pulmonary disease. IEEE J. Biomed. Health Inform. 21(2), 303–311 (2017)
Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: Advances in Knowledge Discovery and Data Mining - 18th Pacific-Asia Conference, PAKDD 2014, Proceedings, Part I, Tainan, Taiwan, 13–16 May 2014, pp. 40–52 (2014)
Fournier-Viger, P., Lin, J.C.-W., Kiran, R.U., Koh, Y.S.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)
Fumarola, F., Lanotte, P.F., Ceci, M., Malerba, D.: Clofast: closed sequential pattern mining using sparse and vertical id-lists. Knowl. Inf. Syst. 48(2), 429–463 (2016)
Ge, J., Xia, Y., Wang, J., Nadungodage, C.H., Prabhakar, S.: Sequential pattern mining in databases with temporal uncertainty. Knowl. Inf. Syst. 51(3), 821–850 (2017)
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.: Freespan: frequent pattern-projected sequential pattern mining. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20–23 August 2000, pp. 355–359 (2000)
Loglisci, C.: Using interactions and dynamics for mining groups of moving objects from trajectory data. Int. J. Geograph. Inf. Sci. 1–33 (2017)
Loglisci, C., Ceci, M., Impedovo, A., Malerba, D.: Mining spatio-temporal patterns of periodic changes in climate data. In: New Frontiers in Mining Complex Patterns - 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, 19 September 2016, Revised Selected Papers, pp. 198–212 (2016)
Loglisci, C., Ceci, M., Malerba, D.: Relational mining for discovering changes in evolving networks. Neurocomputing 150, 265–288 (2015)
Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1), 3:1–3:41 (2010)
Mooney, C., Roddick, J.F.: Sequential pattern mining - approaches and algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)
Schweizer, D., Zehnder, M., Wache, H., Witschel, H.F., Zanatta, D., Rodriguez, M.: Using consumer behavior data to reduce energy consumption in smart homes: applying machine learning to save energy without lowering comfort of inhabitants. In: 14th IEEE International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA, 9–11 December 2015, pp. 1123–1129 (2015)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) Advances in Database Technology - EDBT 1996, 5th International Conference on Extending Database Technology, Proceedings, Avignon, France, 25–29 March 1996, vol. 1057. Lecture Notes in Computer Science, pp. 3–17. Springer (1996)
Viger, P.F., Gomariz, A., Gueniche, T., Soltani, A., Wu, C., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15(1), 3389–3393 (2014)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1/2), 31–60 (2001)
Ziebarth, S., Chounta, I., Hoppe, H.U.: Resource access patterns in exam preparation activities. In: Design for Teaching and Learning in a Networked World - 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Proceedings, Toledo, Spain, 15–18 September 2015, pp. 497–502 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Skenduli, M.P., Loglisci, C., Ceci, M., Biba, M., Malerba, D. (2018). An Empirical Evaluation of Sequential Pattern Mining Algorithms. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-75928-9_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75927-2
Online ISBN: 978-3-319-75928-9
eBook Packages: EngineeringEngineering (R0)