2 options
Data mining in time series databases / editors, Mark Last, Abraham Kandel, Horst Bunke.
- Format:
- Book
- Series:
- Series in machine perception and artificial intelligence ; v. 57.
- Series in machine perception and artificial intelligence ; v. 57
- Language:
- English
- Subjects (All):
- Data mining.
- Distributed databases.
- Physical Description:
- 1 online resource (205 p.)
- Edition:
- 57th ed.
- Place of Publication:
- New Jersey ; London : World Scientific, c2004.
- Language Note:
- English
- Summary:
- Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings.
- Contents:
- Preface; Contents; Chapter 1 Segmenting Time Series: A Survey and Novel Approach E. Keogh, S. Chu, D. Hart and M. Pazzani; Chapter 2 A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences M. L. Hetland; Chapter 3 Indexing of Compressed Time Series E. Fink and K. B. Pratt; Chapter 4 Indexing Time-Series under Conditions of Noise M. Vlachos, D. Gunopulos and G. Das; Chapter 5 Change Detection in Classification Models Induced from Time Series Data G. Zeira, O. Maimon, M. Last and L. Rokach
- Chapter 6 Classification and Detection of Abnormal Events in Time Series of Graphs H. Bunke and M. KraetzlChapter 7 Boosting Interval-Based Literals: Variable Length and Early Classification C. J. Alonso Gonzalez and J. J. Rodriguez Diez; Chapter 8 Median Strings: A Review X. Jiang, H. Bunke and J. Csirik
- Notes:
- Description based upon print version of record.
- Includes bibliographical references.
- ISBN:
- 9786611347765
- 9781281347763
- 1281347760
- 9789812565402
- 981256540X
- 9781423723028
- 1423723023
- OCLC:
- 475947809
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.