| 000 | 06872nam a2200757 i 4500 | ||
|---|---|---|---|
| 001 | 6813100 | ||
| 003 | IEEE | ||
| 005 | 20200413152914.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 140422s2014 caua foab 000 0 eng d | ||
| 020 |
_a9781627053761 _qebook |
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| 020 |
_z9781627053754 _qpaperback |
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| 024 | 7 |
_a10.2200/S00573ED1V01Y201403DMK008 _2doi |
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| 035 | _a(CaBNVSL)swl00403313 | ||
| 035 | _a(OCoLC)877885434 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA276 _b.G866 2014 |
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| 082 | 0 | 4 |
_a519.5 _223 |
| 090 |
_a _bMoCl _e201403DMK008 |
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| 100 | 1 |
_aGupta, Manish., _eauthor. |
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| 245 | 1 | 0 |
_aOutlier detection for temporal data / _cManish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han. |
| 264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2014. |
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| 300 |
_a1 PDF (xviii, 110 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on data mining and knowledge discovery, _x2151-0075 ; _v# 8 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (pages 91-108). | ||
| 505 | 0 | _a1. Introduction and challenges -- 1.1 Temporal outlier examples -- 1.2 Different facets of temporal outlier analysis -- 1.3 Specific challenges for outlier detection for temporal data -- 1.4 Conclusions and summary -- | |
| 505 | 8 | _a2. Outlier detection for time series and data sequences -- 2.1 Outliers in time series databases -- 2.1.1 Direct detection of outlier time series -- 2.1.2 Window-based detection of outlier time series -- 2.1.3 Outlier subsequences in a test time series -- 2.1.4 Outlier points across multiple time series -- 2.2 Outliers within a given time series -- 2.2.1 Points as outliers -- 2.2.2 Subsequences as outliers -- 2.3 Conclusions and summary -- | |
| 505 | 8 | _a3. Outlier detection for data streams -- 3.1 Evolving prediction models -- 3.1.1 Online sequential discounting -- 3.1.2 Dynamic cluster maintenance -- 3.1.3 Dynamic Bayesian networks (DBNS) -- 3.2 Distance-based outliers for sliding windows -- 3.2.1 Distance-based global outliers -- 3.2.2 Distance-based local outliers -- 3.3 Outliers in high-dimensional data streams -- 3.4 Detecting aggregate windows of change -- 3.5 Supervised methods for streaming outlier detection -- 3.6 Conclusions and summary -- | |
| 505 | 8 | _a4. Outlier detection for distributed data streams -- 4.1 Examples and challenges -- 4.2 Sharing data points -- 4.3 Sharing local outliers and other data points -- 4.4 Sharing model parameters -- 4.5 Sharing local outliers and data distributions -- 4.6 Vertically partitioned distributed data -- 4.7 Conclusions and summary -- | |
| 505 | 8 | _a5. Outlier detection for spatio-temporal data -- 5.1 Spatio-temporal outliers (ST-outliers) -- 5.1.1 Density-based outlier detection -- 5.1.2 Outlier detection using spatial scaling -- 5.1.3 Outlier detection using Voronoi diagrams -- 5.2 Spatio-temporal outlier solids -- 5.2.1 Using Kulldorff scan statistic -- 5.2.2 Using image processing -- 5.3 Trajectory outliers -- 5.3.1 Distance between trajectories -- 5.3.2 Direction and density of trajectories -- 5.3.3 Historical similarity -- 5.3.4 Trajectory motifs -- 5.4 Conclusions and summary -- | |
| 505 | 8 | _a6. Outlier detection for temporal network data -- 6.1 Outlier graphs from graph time series -- 6.1.1 Weight independent metrics -- 6.1.2 Metrics using edge weights -- 6.1.3 Metrics using vertex weights -- 6.1.4 Scan statistics -- 6.2 Multi-level outlier detection from graph snapshots -- 6.2.1 Elbows, broken correlations, prolonged spikes, and lightweight stars -- 6.2.2 Outlier node pairs -- 6.3 Community-based outlier detection algorithms -- 6.3.1 Community outliers using community change patterns -- 6.3.2 Change detection using minimum description length -- 6.3.3 Community outliers using evolutionary clustering -- 6.4 Online graph outlier detection algorithms -- 6.4.1 Spectral methods -- 6.4.2 Structural outlier detection -- 6.5 Conclusions and summary -- | |
| 505 | 8 | _a7. Applications of outlier detection for temporal data -- 7.1 Temporal outliers in environmental sensor data -- 7.2 Temporal outliers in industrial sensor data -- 7.3 Temporal outliers in surveillance and trajectory data -- 7.4 Temporal outliers in computer networks data -- 7.5 Temporal outliers in biological data -- 7.6 Temporal outliers in astronomy data -- 7.7 Temporal outliers in web data -- 7.8 Temporal outliers in information network data -- 7.9 Temporal outliers in economics time series data -- 7.10 Conclusions and summary -- | |
| 505 | 8 | _a8. Conclusions and research directions -- Bibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aOutlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on April 22, 2014). | ||
| 650 | 0 | _aOutliers (Statistics) | |
| 650 | 0 | _aTemporal databases. | |
| 653 | _atemporal outlier detection | ||
| 653 | _atime series data | ||
| 653 | _adata streams | ||
| 653 | _adistributed data streams | ||
| 653 | _atemporal networks | ||
| 653 | _aspatiotemporal outliers | ||
| 700 | 1 |
_aGao, Jing., _eauthor. |
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| 700 | 1 |
_aAggarwal, Charu C., _eauthor. |
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| 700 | 1 |
_aHan, Jiawei., _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627053754 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on data mining and knowledge discovery ; _v# 8. _x2151-0075 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813100 |
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttp://dx.doi.org/10.2200/S00573ED1V01Y201403DMK008 |
| 999 |
_c562065 _d562065 |
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