Abstract
The empowerment of the information technologies in many real-world applications has opened to the possibility of tracking complex and evolving phenomena and gather information able to describe such phenomena. For instance, in bio-medical applications, we can monitor a patient and collect data that range from his clinical picture to the laboratory studies on biological products. In this scenario, studying the possible alterations manifested over time becomes thus relevant and, in life sciences, even determinant. In this paper, we investigate the task of determining changes which are regularly repeated over time and we propose a method based on two notions of patterns, emerging patterns and periodic changes. The method works on a time-window model to the end of (i) capturing statistically evident changes and (ii) detecting their periodicity. The method was applied to two typical real-world scenarios with complex dynamic data, that is, Virology and Meteorology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: Evolving networks: Eras and turning points. Intell. Data Anal. 17(1), 27–48 (2013)
Chen, S., Huang, T.C., Lin, Z.: New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports. J. Syst. Softw. 84(10), 1638–1651 (2011)
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52 (1999)
Ferlez, J., Faloutsos, C., Leskovec, J., Mladenic, D., Grobelnik, M.: Monitoring network evolution using MDL. In: Alonso, G., Blakeley, J.A., Chen, A.L.P. (eds.) Proceedings of the 24th International Conference on Data Engineering, ICDE 2008, April 7–12, 2008, Cancún, México, pp. 1328–1330. IEEE (2008)
Furuse, Y., Suzuki, A., Kamigaki, T., Oshitani, H.: Evolution of the m gene of the influenza a virus in different host species: large-scale sequence analysis. Virol. J. 6(67) (2009)
Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 15th International Conference on Data Engineering, Sydney, Austrialia, March 23–26, 1999, pp. 106–115 (1999)
Huang, K., Chang, C.: SMCA: a general model for mining asynchronous periodic patterns in temporal databases. IEEE Trans. Knowl. Data Eng. 17(6), 774–785 (2005)
Lahiri, M., Berger-Wolf, T.Y.: Periodic subgraph mining in dynamic networks. Knowl. Inf. Syst. 24(3), 467–497 (2010)
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Rao, B., Krishnapuram, B., Tomkins, A., Yang, Q. (eds.) Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, July 25–28, 2010, pp. 1099–1108. ACM (2010)
Loglisci, C.: Time-based discovery in biomedical literature: mining temporal links. IJDATS 5(2), 148–174 (2013)
Loglisci, C., Balech, B., Malerba, D.: Discovering variability patterns for change detection in complex phenotype data. In: Esposito, F., Pivert, O., Hacid, M.-S., Rás, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS, vol. 9384, pp. 9–18. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25252-0_2
Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: New Frontiers in Mining Complex Patterns - First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Revised Selected Papers, pp. 185–199 (2012)
Loglisci, C., Ceci, M., Malerba, D.: Relational mining for discovering changes in evolving networks. Neurocomputing, 150, Part A: 265–288 (2015)
Plotkin, G.D.: A note on inductive generalization. Mach. Intell. 5, 153–163 (1970)
Simons, R.A.: Erddap - the environmental research division’s data access program (2011). http://coastwatch.pfeg.noaa.gov/erddap . Pacific Grove, CA:NOAA/NMFS/SWFSC/ERD
Acknowledgements
The authors would like to acknowledge the support of the European Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Loglisci, C., Malerba, D. (2016). Mining Periodic Changes in Complex Dynamic Data Through Relational Pattern Discovery. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-39315-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39314-8
Online ISBN: 978-3-319-39315-5
eBook Packages: Computer ScienceComputer Science (R0)