Anomaly Detection In Temporal Data Mining - Mehmet Yavuz Onat
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Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
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 literatur ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Mehmet Yavuz Onat |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2015 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659797491 |