DATA MINING IN TIME SERIES DATABASES
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DATA MINING IN TIME SERIES DATABASES
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.
Contents:
- 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 Gonz�lez & J J Rodr�guez Diez)
- Median Strings: A Review (X Jiang et al.)
View Full Text (3,709 KB)
Readership: Graduate students, researchers and practitioners in the
fields of data mining, machine learning, databases and statistics.
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204pp
Pub. date: Jun 2004
eISBN 981-256-540-X
Price: US$86
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