Data mining in time series databases [[electronic resource] /] / editors, Mark Last, Abraham Kandel, Horst Bunke

• Pubblicazione: New Jersey ; ; London, : World Scientific, c2004
• Edizione: 57th ed.
• Descrizione fisica: 1 online resource (205 p.)
• Disciplina: 006.33
• Soggetto topico: Data mining; Distributed databases
• Classificazione: 54.64
• Altri autori: LastMark KandelAbraham BunkeHorst
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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
• Sommario/riassunto: 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.
• Titolo autorizzato: Data mining in time series databases
• ISBN: 1-281-34776-0; 9786611347765; 981-256-540-X; 1-4237-2302-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910783223703321
• Lo trovi qui: Univ. Federico II
• Serie: Series in machine perception and artificial intelligence ; ; v. 57.