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Data mining in time series databases /
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 manual examines state-of-the-art methodology for mining time series databases. The novel data mining methods presented in t...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New Jersey ; London :
World Scientific,
©2004.
|
| Colección: | Series in machine perception and artificial intelligence ;
v. 57. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Data mining in time series databases / |c editors, Mark Last, Abraham Kandel, Horst Bunke. |
| 260 | |a New Jersey ; |a London : |b World Scientific, |c ©2004. | ||
| 300 | |a 1 online resource (xi, 192 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Series in machine perception and artificial intelligence ; |v v. 57 | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | 0 | |t Segmenting time series : a survey and novel approach / |r E. Keogh [and others] -- |t A survey of recent methods for efficient retrieval of similar time sequences / |r M.L. Hetland -- |t Indexing of compressed time series / |r E. Fink and K.B. Pratt -- |t Indexing time-series under conditions of noise / |r M. Vlachos, D. Gunopulos, and G. Das -- |t Change detection in classification models induced from time series data / |r G. Zeira [and others] -- |t Classification and detection of abnormal events in time series of graphs / |r H. Bunke and M. Kraetzl -- |t Boosting interval-based literals : variable length and early classification / |r C.J. Alonso González and J.J. Rodríguez Diez -- |t Median strings : a review / |r X. Jiang, H. Bunke, and J. Csirik. |
| 588 | 0 | |a Print version record. | |
| 520 | |a 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 manual examines 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 text 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Distributed databases. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Bases de données réparties. | |
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| 650 | 7 | |a Distributed databases |2 fast | |
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| 650 | 7 | |a Bases de données réparties. |2 rvm | |
| 700 | 1 | |a Last, Mark. | |
| 700 | 1 | |a Kandel, Abraham. | |
| 700 | 1 | |a Bunke, Horst. | |
| 776 | 0 | 8 | |i Print version: |t Data mining in time series databases. |d New Jersey ; London : World Scientific, ©2004 |z 9812382909 |w (OCoLC)56760428 |
| 830 | 0 | |a Series in machine perception and artificial intelligence ; |v v. 57. | |
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