Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Anomaly Detection In Temporal Data Mining

Anomaly Detection In Temporal Data Mining

Onat Mehmet Yavuz

Paperback

General Mathematics

ISBN10: 3659797499
ISBN13: 9783659797491
Publisher: Lap Lambert Academic Pub
Published: Dec 9 2015
Pages: 72
Weight: 0.26
Height: 0.17 Width: 6.00 Depth: 9.00
Language: English
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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