Data Mining in Time Series Databases

An examination of state-of-the-art methodology for mining time series databases. The data mining methods presented include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described.

Sommario

A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings - A Review (X Jiang et al); Change Detection in Classification Models of Data Mining (G Zeira et al).

Dettagli sul prodotto

• Autori: Kandel Abraham
• Con la collaborazione di: Horst Bunke (Editore), Abraham Kandel (Editore), Mark Last (Editore)
• Editore: World Scientific
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 01.01.2004
• Pagine: 204
• Dimensioni: 157 mm x 229 mm x 18 mm
• Peso: 431 g
• Serie: Series in Machine Perception a
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica