Outlier Detection for Temporal Data

Description du livre Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 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 132 pp. Englisch. N� de r�f. du vendeur 9783031007774

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 37,44

Autre devise

Ajouter au panier

Frais de port : EUR 23

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Image fournie par le vendeur

Outlier Detection for Temporal Data

Manish Gupta

Edit� par Springer International Publishing (2014)

ISBN 10 : 3031007778 ISBN 13 : 9783031007774

Neuf Taschenbuch Quantit� disponible : 1

Vendeur :

AHA-BUCH GmbH
(Einbeck, Allemagne)

Evaluation vendeur

Evaluation du vendeur :

Description du livre Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. 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. N� de r�f. du vendeur 9783031007774

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 40,57

Autre devise

Ajouter au panier

Frais de port : EUR 32,99

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Image fournie par le vendeur

Outlier Detection for Temporal Data

Gupta, Manish|Gao, Jing|Aggarwal, Charu|Han, Jiawei

Edit� par Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer (2014)

ISBN 10 : 3031007778 ISBN 13 : 9783031007774

Neuf Couverture souple Quantit� disponible : > 20

impression � la demande

Vendeur :

moluna
(Greven, Allemagne)

Evaluation vendeur

Evaluation du vendeur :

Description du livre Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 re. N� de r�f. du vendeur 608129163

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 34,41

Autre devise

Ajouter au panier

Frais de port : EUR 48,99

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Articles li�s � Outlier Detection for Temporal Data

Outlier Detection for Temporal Data - Couverture souple

Gupta, Manish; Gao, Jing; Aggarwal, Charu; Han, Jiawei

Acheter neuf

Meilleurs r�sultats de recherche sur AbeBooks

Outlier Detection for Temporal Data (Synthesis Lectures on Data Mining and Knowledge Discovery) by Gupta, Manish, Gao, Jing, Aggarwal, Charu, Han, Jiawei [Paperback ]

Outlier Detection for Temporal Data (Paperback or Softback)

Outlier Detection for Temporal Data (Synthesis Lectures on Data Mining and Knowledge Discovery)

Outlier Detection for Temporal Data

Outlier Detection for Temporal Data

Outlier Detection for Temporal Data

Outlier Detection for Temporal Data