Data mining in time series databases
Author(s)
Bibliographic Information
Data mining in time series databases
（Series in machine perception and artificial intelligence / editors, H. Bunke, P.S.P. Wang, v. 57）
World Scientific, c2004
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Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table 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).
by "Nielsen BookData"
