By Mark Last, Abraham Kandel, Horst Bunke
This skinny booklet provides 8 educational papers discussing dealing with of sequences. i didn't locate any of them attention-grabbing by itself or reliable as a survey, yet teachers doing examine in computing device studying may possibly disagree. when you are one, you possibly can get the unique papers. when you are a practitioner, cross with out a moment idea.
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Extra info for Data mining in time series databases
In Figure 4, we give a procedure for selecting important points, which takes linear time and constant memory. It outputs the values and indices of all important points, as well as the ﬁrst and last point of the series. This procedure can process new points as they arrive, without storing the Fig. 2. 9 Important points for 91% compression (left) and 94% compression (right). am am · R am /R am i Fig. 3. time j i time j Important minimum (left) and important maximum (right). 48 E. Fink and K. B. Pratt IMPORTANT-POINTS—Top-level function for ﬁnding important points.
Journal of Knowledge and Information Systems, 3(3), 263–286. 13. Keogh, E. and Pazzani, M. (1998). An Enhanced Representation of Time Series which Allows Fast and Accurate Classiﬁcation, Clustering and Relevance Feedback. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, AAAI Press, pp. 239–241. 14. Keogh, E. and Pazzani, M. (1999). Relevance Feedback Retrieval of Time Series Data. Proceedings of the 22th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pp.
2000)], but it is generally assumed that the values are real numbers. This assumption is a requirement for most of the methods described in this chapter. The only requirement of the timestamps is that they be non-decreasing (or, in some applications, strictly increasing) with respect to the sequence indices: ti ≤ tj ⇔ i ≤ j. (1) In some methods, an additional assumption is that the elements are equi-spaced: for every two consecutive elements xi and xi+1 we have ti+1 − ti = ∆, (2) where ∆ (the sampling rate of x) is a (positive) constant.
Data mining in time series databases by Mark Last, Abraham Kandel, Horst Bunke