Data mining in time series databases by Mark Last, Abraham Kandel, Horst Bunke

By Mark Last, Abraham Kandel, Horst Bunke

This skinny ebook provides 8 educational papers discussing dealing with of sequences. i didn't locate any of them attention-grabbing by itself or solid as a survey, yet lecturers doing examine in laptop studying may perhaps disagree. while you're one, you probably can get the unique papers. when you are a practitioner, cross with no moment suggestion.

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Thus, first-order landmarks are extrema, second-order landmarks are inflection points, and so forth. A smothing technique is also introduced, which lets certain landmarks be overshadowed by others. For instance, local extrema representing small fluctuations may not be as important as a global maximum or minimum. Figure 9 shows an approximated time sequence, reconstructed from a twelve-dimensional landmark signature. One of the main contributions of Perng et al. (2000) is to show that for suitable selections of landmark features, the model is invariant with respect to the following transformations: • • • • • Shifting Uniform amplitude scaling Uniform time scaling Non-uniform time scaling (time warping) Non-uniform amplitude scaling It is also possible to allow for several of these transformations at once, by using the intersection of the features allowed for each of them.

H. (1999). A Fast Algorithm on Average for All-Against-All Sequence Matching. Proc. 6th String Processing and Information Retrieval Symposium (SPIRE), pp. 16–23. 4. Baeza-Yates, R. and Ribeiro-Neto, B. (1999). Modern Information Retrieval. ACM Press/Addison–Wesley Longman Limited. 5. Chakrabarti, K. and Mehrotra, S. (1999). The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces. Proc. 15th Int. Conf. on Data Engineering (ICDE), pp. 440–447. 6. Chan, K. W. (1999). Efficient Time Series Matching by Wavelets.

1997), Bozkaya and Ozsoyoglu (1999)]. Park et al. (2001) indexed series by their local extrema and by properties of the segments between consecutive extrema. Li et al. (1998) proposed a retrieval technique based on a multi-level abstraction hierarchy of features. Aggarwal and Yu (2000) considered grid structures, but found that the grid performance is often no better than exhaustive search. They also showed that exhaustive search among compressed series is often faster than sophisticated indexing techniques.

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