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.

List of contents

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).

Product details

• Authors: Kandel Abraham
• Assisted by: Horst Bunke (Editor), Abraham Kandel (Editor), Mark Last (Editor)
• Publisher: World Scientific
• Languages: English
• Product format: Hardback
• Released: 01.01.2004
• No. of pages: 204
• Dimensions: 157 mm x 229 mm x 18 mm
• Weight: 431 g
• Series: Series in Machine Perception a
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT