By Mark Last, Abraham Kandel, Horst Bunke
This skinny publication provides 8 educational papers discussing dealing with of sequences. i didn't locate any of them fascinating by itself or reliable as a survey, yet teachers doing study in computing device studying may well disagree. when you are one, you probably can get the unique papers. when you are a practitioner, cross with out a moment idea.
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Extra resources for Data mining in time series databases
J. J. (1999b). Scaling up Dynamic Time Warping to Massive Datasets. Proc. 3rd European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD), pp. 1–11. 20. J. J. (2000). A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. Proc. of the 4th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 122–133. 21. J. and Smyth, P. (1997). A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. Proc. 3rd Int.
2000) applied Euclidean distance to compare feature vectors containing color, texture, and shape of video data. This technique works well when all features have the same units of scale [Goldin and Kanellakis (1995)], but it is often ineﬀective for combining disparate features. An alternative deﬁnition of similarity is based on bounding rectangles; two series are similar if their bounding rectangles are similar. It allows fast pruning of clearly dissimilar series [Perng et al. (2000), Lee et al.
Conf. on Scientiﬁc and Statistical Database Management (SSDBM), pp. 56–67. 19. J. J. (1999b). Scaling up Dynamic Time Warping to Massive Datasets. Proc. 3rd European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD), pp. 1–11. 20. J. J. (2000). A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. Proc. of the 4th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 122–133. 21. J. and Smyth, P. (1997). A Probabilistic Approach to Fast Pattern Matching in Time Series Databases.