Titlebook: Discovery of Ill–Known Motifs in Time Series Data; Sahar Deppe Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

Infect

銀版照相

In-Situ

https://doi.org/10.1007/978-1-4419-0228-3In the previous sections, the theoretical methods and their properties that contributed to this dissertation have been described. In this chapter, experiments are performed in order to evaluate the performance of KITE. All the examinations are benchmarked against six state-of-the-art algorithms, as explained in Chapter 4.

侵蝕

起來(lái)了

一個(gè)攪動(dòng)不安

Mathematical Discourse as Group Cognitiontion. The data representation step aims to reduce the data’s size or to approximate it without losing relevant information. In the final step, the similarity measurement step, motifs are detected by quantifying their similarity based on a given threshold [EsA12,Mue14,ToL17b].

ethereal

Elizabeth S. Charles,Wesley Shumariverse areas. This topic attracts several researchers from the domain of data and knowledge discovery. Data mining and pattern recognition tasks aim to provide information derived from time series [BeR14, BLB.17, FaV17, AAJ.19, AlA20].

想象

松軟

大洪水

978-3-662-64214-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE