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Outlier detection for temporal data /
Outlier (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...
| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2014.
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| Series: | Synthesis digital library of engineering and computer science.
Synthesis lectures on data mining and knowledge discovery ; # 8. |
| Subjects: | |
| Online Access: | Abstract with links to full text |
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| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 140422s2014 caua foab 000 0 eng d | ||
| 020 | |a 9781627053761 |q ebook | ||
| 020 | |z 9781627053754 |q paperback | ||
| 024 | 7 | |a 10.2200/S00573ED1V01Y201403DMK008 |2 doi | |
| 035 | |a (CaBNVSL)swl00403313 | ||
| 035 | |a (OCoLC)877885434 | ||
| 040 | |a CaBNVSL |b eng |e rda |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a QA276 |b .G866 2014 | |
| 082 | 0 | 4 | |a 519.5 |2 23 |
| 100 | 1 | |a Gupta, Manish., |e author. | |
| 245 | 1 | 0 | |a Outlier detection for temporal data / |c Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han. |
| 264 | 1 | |a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c 2014. | |
| 300 | |a 1 PDF (xviii, 110 pages) : |b illustrations. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a Synthesis lectures on data mining and knowledge discovery, |x 2151-0075 ; |v # 8 | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 500 | |a Series from website. | ||
| 504 | |a Includes bibliographical references (pages 91-108). | ||
| 505 | 0 | |a 1. 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 | |a 2. 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 | |a 3. 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 | |a 4. 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 | |a 5. 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 | |a 6. 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 | |a 7. 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 | |a 8. Conclusions and research directions -- Bibliography -- Authors' biographies. | |
| 506 | |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | |a Compendex | |
| 510 | 0 | |a Google book search | |
| 510 | 0 | |a Google scholar | |
| 510 | 0 | |a INSPEC | |
| 520 | 3 | |a Outlier (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 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 538 | |a System requirements: Adobe Acrobat Reader. | ||
| 588 | |a Title from PDF title page (viewed on April 22, 2014). | ||
| 650 | 0 | |a Outliers (Statistics) | |
| 650 | 0 | |a Temporal databases. | |
| 653 | |a data streams | ||
| 653 | |a distributed data streams | ||
| 653 | |a spatiotemporal outliers | ||
| 653 | |a temporal networks | ||
| 653 | |a temporal outlier detection | ||
| 653 | |a time series data | ||
| 700 | 1 | |a Aggarwal, Charu C., |e author. | |
| 700 | 1 | |a Gao, Jing., |e author. | |
| 700 | 1 | |a Han, Jiawei., |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781627053754 |
| 830 | 0 | |a Synthesis digital library of engineering and computer science. | |
| 830 | 0 | |a Synthesis lectures on data mining and knowledge discovery ; |v # 8. |x 2151-0075 | |
| 856 | 4 | 8 | |3 Abstract with links to full text |u http://dx.doi.org/10.2200/S00573ED1V01Y201403DMK008 |
| 942 | |c EB | ||
| 999 | |c 81090 |d 81090 | ||
| 952 | |0 0 |1 0 |4 0 |7 0 |9 73110 |a MGUL |b MGUL |d 2016-03-20 |l 0 |r 2016-03-20 |w 2016-03-20 |y EB | ||