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...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey ; London :
World Scientific,
©2004.
|
| Schriftenreihe: | Series in machine perception and artificial intelligence ;
v. 57. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (xi, 192 pages) : illustrations. |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1423723023 9781423723028 9789812382900 9812382909 1281347760 9781281347763 |
Internformat
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| 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. | ||
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| 700 | 1 | |a Last, Mark. | |
| 700 | 1 | |a Kandel, Abraham. | |
| 700 | 1 | |a Bunke, Horst. | |
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| contents | Segmenting time series : a survey and novel approach / A survey of recent methods for efficient retrieval of similar time sequences / Indexing of compressed time series / Indexing time-series under conditions of noise / Change detection in classification models induced from time series data / Classification and detection of abnormal events in time series of graphs / Boosting interval-based literals : variable length and early classification / Median strings : a review / |
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| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xi, 192 pages) : illustrations. |
| psigel | ZDB-4-EBA |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Data mining in time series databases / editors, Mark Last, Abraham Kandel, Horst Bunke. New Jersey ; London : World Scientific, ©2004. 1 online resource (xi, 192 pages) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Series in machine perception and artificial intelligence ; v. 57 Includes bibliographical references. Segmenting time series : a survey and novel approach / E. Keogh [and others] -- A survey of recent methods for efficient retrieval of similar time sequences / M.L. Hetland -- Indexing of compressed time series / E. Fink and K.B. Pratt -- Indexing time-series under conditions of noise / M. Vlachos, D. Gunopulos, and G. Das -- Change detection in classification models induced from time series data / G. Zeira [and others] -- Classification and detection of abnormal events in time series of graphs / H. Bunke and M. Kraetzl -- Boosting interval-based literals : variable length and early classification / C.J. Alonso González and J.J. Rodríguez Diez -- Median strings : a review / X. Jiang, H. Bunke, and J. Csirik. Print version record. 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. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Distributed databases. http://id.loc.gov/authorities/subjects/sh88000865 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Bases de données réparties. COMPUTERS Expert Systems. bisacsh Data mining fast Distributed databases fast Exploration de données (Informatique) rvm Bases de données réparties. rvm Last, Mark. Kandel, Abraham. Bunke, Horst. Print version: Data mining in time series databases. New Jersey ; London : World Scientific, ©2004 9812382909 (OCoLC)56760428 Series in machine perception and artificial intelligence ; v. 57. http://id.loc.gov/authorities/names/n91107585 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=137593 Volltext |
| spellingShingle | Data mining in time series databases / Series in machine perception and artificial intelligence ; Segmenting time series : a survey and novel approach / A survey of recent methods for efficient retrieval of similar time sequences / Indexing of compressed time series / Indexing time-series under conditions of noise / Change detection in classification models induced from time series data / Classification and detection of abnormal events in time series of graphs / Boosting interval-based literals : variable length and early classification / Median strings : a review / Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Distributed databases. http://id.loc.gov/authorities/subjects/sh88000865 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Bases de données réparties. COMPUTERS Expert Systems. bisacsh Data mining fast Distributed databases fast Exploration de données (Informatique) rvm Bases de données réparties. rvm |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh88000865 https://id.nlm.nih.gov/mesh/D057225 |
| title | Data mining in time series databases / |
| title_alt | Segmenting time series : a survey and novel approach / A survey of recent methods for efficient retrieval of similar time sequences / Indexing of compressed time series / Indexing time-series under conditions of noise / Change detection in classification models induced from time series data / Classification and detection of abnormal events in time series of graphs / Boosting interval-based literals : variable length and early classification / Median strings : a review / |
| title_auth | Data mining in time series databases / |
| title_exact_search | Data mining in time series databases / |
| title_full | Data mining in time series databases / editors, Mark Last, Abraham Kandel, Horst Bunke. |
| title_fullStr | Data mining in time series databases / editors, Mark Last, Abraham Kandel, Horst Bunke. |
| title_full_unstemmed | Data mining in time series databases / editors, Mark Last, Abraham Kandel, Horst Bunke. |
| title_short | Data mining in time series databases / |
| title_sort | data mining in time series databases |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Distributed databases. http://id.loc.gov/authorities/subjects/sh88000865 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Bases de données réparties. COMPUTERS Expert Systems. bisacsh Data mining fast Distributed databases fast Exploration de données (Informatique) rvm Bases de données réparties. rvm |
| topic_facet | Data mining. Distributed databases. Data Mining Exploration de données (Informatique) Bases de données réparties. COMPUTERS Expert Systems. Data mining Distributed databases |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=137593 |
| work_keys_str_mv | AT lastmark dataminingintimeseriesdatabases AT kandelabraham dataminingintimeseriesdatabases AT bunkehorst dataminingintimeseriesdatabases |