000 05344nam a22005415i 4500
001 978-3-031-01905-0
003 DE-He213
005 20240730164926.0
007 cr nn 008mamaa
008 220601s2014 sz | s |||| 0|eng d
020 _a9783031019050
_9978-3-031-01905-0
024 7 _a10.1007/978-3-031-01905-0
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aGupta, Manish.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986688
245 1 0 _aOutlier Detection for Temporal Data
_h[electronic resource] /
_cby Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXVIII, 110 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
505 0 _aPreface -- Acknowledgments -- Figure Credits -- Introduction and Challenges -- Outlier Detection for Time Series and Data Sequences -- Outlier Detection for Data Streams -- Outlier Detection for Distributed Data Streams -- Outlier Detection for Spatio-Temporal Data -- Outlier Detection for Temporal Network Data -- Applications of Outlier Detection for Temporal Data -- Conclusions and Research Directions -- Bibliography -- Authors' Biographies .
520 _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. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data.In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neuralnetworks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_986690
650 2 4 _aStatistics.
_914134
700 1 _aGao, Jing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986691
700 1 _aAggarwal, Charu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986693
700 1 _aHan, Jiawei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_92109
710 2 _aSpringerLink (Online service)
_986697
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007774
776 0 8 _iPrinted edition:
_z9783031030338
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_986698
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01905-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c85993
_d85993