Abstract
To be successful with certain classification problems or knowledge discovery tasks it is not sufficient to look at the available variables at a single point in time, but their development has to be traced over a period of time. It is shown that patterns and sequences of labeled intervals represent a particularly well suited data format for this purpose. An extension of existing classifiers is proposed that enables them to handle this kind of sequential data. Compared to earlier approaches the expressiveness of the pattern language (using Allen et al.’s interval relationships) is increased, which allows the discovery of many temporal patterns common to real-world applications.
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References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of 11th Int. Conf. on Data Engineering, Taipei, Taiwan, pp. 3–14 (March 1995)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proc. of the 1st ACM SIGKDD Int. Conf. on Knowl. Discovery and Data Mining, Menlo Park, Calif., pp. 210–215 (1995)
Allen, J.F., Kautz, H.A., Pelavin, R.N., Tenenberg, J.D.: Reasoning about Plans. Morgan Kaufmann Publishers, San Francisco (1991)
Höppner, F.: Discovery of temporal patterns – learning rules about the qualitative behaviour of time series. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 192–203. Springer, Heidelberg (2001)
Kam, P.S., Fu, A.W.C.: Discovering temporal patterns for interval-based events. In: Kambayashi, Y., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 317–326. Springer, Heidelberg (2000)
Villafane, R., Hua, K.A., Tran, D., Maulik, B.: Knowledge discovery from series of interval events. Journal of Intelligent Information Systems 15(1), 71–89 (2000)
Freksa, C.: Temporal reasoning based on semi-intervals. Artificial Intelligence 54(1), 199–227 (1992)
Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Trans. on Knowledge and Data Engineering 4(4), 301–316 (1992)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
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Höppner, F., Topp, A. (2007). Classification Based on the Trace of Variables over Time. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_74
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DOI: https://doi.org/10.1007/978-3-540-77226-2_74
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