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Series in Machine Perception and Artificial Intelligence - Vol. 57
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 González & J J Rodríguez Diez)
- Median Strings: A Review (X Jiang et al.)
Readership: Graduate students, researchers and practitioners in the fields of
data mining, machine learning, databases and statistics.
| 204pp |
Pub. date: Jun 2004 |
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