Abstract
There is a growing need to analyse sets of complex data, i.e., data in which the individual data items are (semi-) structured collections of data themselves, such as sets of time-series. To perform such analysis, one has to redefine familiar notions such as similarity on such complex data types. One can do that either on the data items directly, or indirectly, based on features or patterns computed from the individual data items. In this paper, we argue that wavelet decomposition is a general tool for the latter approach.
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References
Dario Benedetto, Emanuele Caglioti, and Victor Loreto. Language trees and zipping. Physical Review Letters, 88(4), 2002.
C.H. Bennet, P. Gács, M. Li, P.M.B. Vitányi, and W. Zurek. Information distance. IEEE Trans. on Information Theory, 44(4):1407–1423, 1998.
R.J. Bolton and D.J. Hand. Unsupervised profiling methods for fraud detection. Credit Scoring and Control VII, Edinburgh, 2001.
R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis-Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
David Hand, Heikki Mannila, and Padhraic Smyth. Principles of Data Mining. MIT Press, 2001.
Tamer Kahveci and Ambuj K. Singh. An efficient index structure for string databases. In Proceedings of the 27th VLDB, pages 351–360. Morgan Kaufmann, 2001.
Ming Li, Xin Li, Bin Ma, and Paul Vitányi. Normalized Information Distance and Whole Mitochondrial Genome Phylogeny Analysis. arXiv:cs.CC/0111054v1, 2001.
Ming Li and Paul Vitányi. An Introduction to Kolmogorov Complexity and its Applications. Springer Verlag, 1993.
S.G. Mallat and W.I. Wang. Singularity detection and processing with wavelets. IEEE Trans. on Information Theory, 38, 1992.
Gordon E. Moore. Cramming more components onto integrated circuits. Electronics, 38(8), 1965.
Frederick Mosteller and David L. Wallace. Applied Bayesian and Classical Inference-The Case of The Federalist Papers. Springer Verlag, 1984.
R. Todd Ogden. Essential Wavelets for Statistical Applications and Data Analysis. Birkhäuser, 1997.
Simone Santini. Exploratory Image Databases-Content-Based Retrieval. Academic Press, 2001.
Simeon J. Simoff and Osmar R. Zaïane, eds.. Proceedings of the First International Workshop on Multimedia Data Mining,MDM/KDD2000. http://www.cs.ualberta.ca/zaiane/mdm_kdd2000/, 2000.
Zbigniew R. Struzik and Arno Siebes. Wavelet transform based multifractal formalism in outlier detection and localisation for financial time series. Physica A: Statistical Mechanics and its Applications, 309(3–4):388–402, 2002.
Z.R. Struzik and A.P.J.M. Siebes. The haar wavelet in the time series similarity paradigm. In Proceedings of PKDD99, LNAI 1704, pages 12–22. Springer Verlag, 1999.
Osmar R. Zaïane and Simeon J. Simoff, eds.. Proceedings of the Second International Workshop on Multimedia Data Mining, MDM/KDD2001. http://www.acm.org/sigkdd/proceedings/mdmkdd01, 2001.
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Siebes, A., Struzik, Z. (2002). Complex Data: Mining Using Patterns. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_3
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DOI: https://doi.org/10.1007/3-540-45728-3_3
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