Publications

Data Mining In Time Series Databases

edited by Mark Last (Ben-Gurion U., Israel), Abraham Kandel (Tel-Aviv U., Israel & U. of South Florida, USA), and Horst Bunke (U. 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.)

Readership: Graduate students, researchers and practitioners in the fields of data mining, machine learning, databases and statistics.

204pp. Pub. date: Jun 2004

ISBN 981-238-290-9