edited by Mark Last (Ben-Gurion University of the Negev, Israel), Abraham Kandel (Tel-Aviv University Israel & University of South Florida, USA), & Horst Bunke (University of Bern, Switzerland)

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book 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 and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed.

• Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.)
• A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (M L Hetland)
• Indexing of Compressed Time Series (E Fink & K Pratt)
• Indexing Time-Series under Conditions of Noise (M Vlachos et al.)
• Change Detection in Classification Models Induced from Time Series Data (G Zeira et al.)
• Classification and Detection of Abnormal Events in Time Series of Graphs (H Bunke & M Kraetzl)
• Boosting Interval-Based Literals: Variable Length and Early Classification (C J Alonso González & J J Rodríguez Diez)
• Median Strings: A Review (X Jiang et al.)