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008			220601s2014 sz | s |||| 0|eng d
020			_a9783031019050
024	7		_a10.1007/978-3-031-01905-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA276 _b2014 EB
100	1		_aGupta, Manish _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aOutlier Detection for Temporal Data _cby Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XVIII, 110 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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, neural networks), 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.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9138936 _aEstadística matemática
650		7	_2embne _9138966 _aBases de datos
700	1		_aGao, Jing _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aAggarwal, Charu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aHan, Jiawei _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031007774
776	0	8	_iPrinted edition: _z9783031030338
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01905-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI