Artículos relacionados a Anomaly Detection In Temporal Data Mining

Anomaly Detection In Temporal Data Mining - Tapa blanda

Onat, Mehmet Yavuz

 
9783659797491: Anomaly Detection In Temporal Data Mining
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ISBN 10: 3659797499 ISBN 13: 9783659797491
Editorial: LAP LAMBERT Academic Publishing, 2015

Sinopsis

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.

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Reseña del editor

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.

Biografía del autor

Birth: 02.09.1988, Ankara (Turkey). Bachelor and Master's Degree: Statistics, Dokuz Eylül University, İzmir (Turkey). Temporal data mining is a growing field. I would like to continue my study during my PhD and introduce a brand-new algorithm or representation technique to the field.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2015
Idioma
Inglés
ISBN 10 
3659797499
ISBN 13 
9783659797491
Encuadernación
Tapa blanda
Número de páginas
72
Contacto del fabricante
no disponible
Persona responsable
no disponible