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

judiciousness

Preliminaries,ies is included since time series constitute this dissertation’s main scope. In the context of data mining and knowledge discovery, time series analysis does not only aim to extract statistics but mainly to discover the characteristics and meaningful information from the data by employing tasks such

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magnanimity

State of the Art in Time Series Motif Discovery,iverse 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].

大方一點(diǎn)

Distortion-Invariant Motif Discovery,nd the research gaps provided in Chapter 4, KITE’s approach is proposed in this section. KITE aims to overcome the limitations of the existing methods regarding detecting illknown motifs by means of its steps.

粗魯性質(zhì)

Conclusion and Outlook, attractive area for several researchers. By increasing the power of computers to collect and compute data, the need for methods that analyse the data and extract knowledge from it is growing. This issue is addressed by tasks such as clustering, classification, query by content, anomaly detection, a

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繁榮中國(guó)

Damian O. Elias,Andrew C. Masonmount of data. According to IBM [IBM20], over 2.5 quintillion bytes of data are created every single day, obtained from different fields such as economics, medicine and epidemiology, industry and telecommunications, geographical and physical science [PfL18, ElB18, CTC.19, SGO20]. These data are main

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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].

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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].