ISBN: 9783659797491 - anomaly detection in temporal data mining (14 resultados)

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Taschenbuch. Condición: Neu. Anomaly Detection In Temporal Data Mining | Mehmet Yavuz Onat | Taschenbuch | 72 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659797491 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. …

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Temporal data mining is a title for data mining techniques executed over temporal data. The major goals of temporal data mining are; indexing, clustering, classification, prediction, summarization, anomaly detection and segmentation. In temporal data, anomaly detection or novelty detection is the identification of interesting patterns. Several anomaly detection algorithms have been proposed in the literature. However, there are limited number of studies that compare these methods. In this study, Heuristically Ordered Time series using Symbolic Aggregate Approximation (HOT-SAX), Pattern Anomaly Value (PAV), Wavelet and Augmented Trie (WAT) and Multi-Scale Abnormal Pattern Detection Algorithm (MPAV) anomaly detection methods were compared by using synthetic and real temporal data sets. Also, temporal data representation techniques were compared in terms of anomaly detection. R statistical programming language was used for analysis. 72 pp. Englisch. …

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Temporal data mining is a title for data mining techniques executed over temporal data. The major goals of temporal data mining are; indexing, clustering, classification, prediction, summarization, anomaly detection and segmentation. In temporal data, anomaly detection or novelty detection is the identification of interesting patterns. Several anomaly detection algorithms have been proposed in the literature. However, there are limited number of studies that compare these methods. In this study, Heuristically Ordered Time series using Symbolic Aggregate Approximation (HOT-SAX), Pattern Anomaly Value (PAV), Wavelet and Augmented Trie (WAT) and Multi-Scale Abnormal Pattern Detection Algorithm (MPAV) anomaly detection methods were compared by using synthetic and real temporal data sets. Also, temporal data representation techniques were compared in terms of anomaly detection. R statistical programming language was used for analysis.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Onat Mehmet YavuzBirth: 02.09.1988, Ankara (Turkey). Bachelor and Master s Degree: Statistics, Dokuz Eyluel University, Izmir (Turkey). Temporal data mining is a growing field. I would like to continue my study during my PhD and intr.…

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Temporal data mining is a title for data mining techniques executed over temporal data. The major goals of temporal data mining are; indexing, clustering, classification, prediction, summarization, anomaly detection and segmentation. In temporal data, anomaly detection or novelty detection is the identification of interesting patterns. Several anomaly detection algorithms have been proposed in the literature. However, there are limited number of studies that compare these methods. In this study, Heuristically Ordered Time series using Symbolic Aggregate Approximation (HOT-SAX), Pattern Anomaly Value (PAV), Wavelet and Augmented Trie (WAT) and Multi-Scale Abnormal Pattern Detection Algorithm (MPAV) anomaly detection methods were compared by using synthetic and real temporal data sets. Also, temporal data representation techniques were compared in terms of anomaly detection. R statistical programming language was used for analysis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.…