Abstract
This paper is concerned with time series of graphs and proposes a novel scheme that is able to predict the presence or absence of nodes in a graph. The proposed scheme is based on decision trees that are induced from a training set of sample graphs. The work is motivated by applications in computer network monitoring. However, the proposed prediction method is generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Last, M., Kandel, A., Bunke, H. (eds.): Data Mining in Time Series Databases. World Scientific, Singapore (2004)
Keogh, E., et al.: Segmenting time series: A survey and novel approach. In: [1], pp. 1–21
Kahveci, T., Singh, K.: Optimizing similarity search for arbitrary length time series queries. IEEE Trans. KDE 16(2), 418–433 (2004)
Vlachos, M., et al.: Indexing time-series under conditions of noise. In: [1], pp. 67–100
Zeira, G., et al.: Change detection in classification models induced from time series data. In: [1], pp. 101–125
Tanaka, H., Uehara, K.: Discover motifs in multi-dimensionaltime-series using the principle component analysis and the MDL principle. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS (LNAI), vol. 2734, pp. 252–265. Springer, Heidelberg (2003)
Yang, J., Wang, W., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. KDE 15(3), 613–628 (2003)
Schmidt, R., Gierl, L.: Temporal abstractions and case-based reasoning for medical course data: two prognostic applications. In: Perner, P. (ed.) MLDM 2001. LNCS (LNAI), vol. 2123, pp. 23–34. Springer, Heidelberg (2001)
Fung, G.P.C.F., Yu, D.X., Lam, W.: News sensitive stock trend prediction. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 481–493. Springer, Heidelberg (2002)
Povinelli, R.F., Feng, X.: A new temporal pattern identification method for characterization and prediction of complex time series events. IEEE Trans. KDE 15(2), 339–352 (2003)
Bunke, H.: Graph-based tools for data mining and machine learning. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS (LNAI), vol. 2734, pp. 7–19. Springer, Heidelberg (2003)
Hayes, S.: Analysing network performance management. IEEE Communications Magazine 31(5), 52–58 (1993)
Higginbottom, G.N.: Performance Evaluation of Communication Networks. Artech House, Massachusetts (1998)
Bunke, H., Kraetzl, M., Shoubridge, P., Wallis, W.: Detection of abnormal change in time series of graphs. Journal of Interconnection Networks 3(1, 2), 85–101 (2002)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgen Kaufmann Publ., San Francisco (1993)
Dickinson, P., Bunke, H., Dadej, A., Kraetzl, M.: Matching graphs with unique node labels. Accepted for publication in Pattern Analysis and Applications
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bunke, H., Dickinson, P., Irniger, C., Kraetzl, M. (2005). Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_36
Download citation
DOI: https://doi.org/10.1007/11510888_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
eBook Packages: Computer ScienceComputer Science (R0)