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Books > Computing & IT > Applications of computing > Databases > Data mining
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Outlier Detection for Temporal Data (Paperback)
Loot Price: R1,066
Discovery Miles 10 660
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Outlier Detection for Temporal Data (Paperback)
Series: Synthesis Lectures on Data Mining and Knowledge Discovery
Expected to ship within 10 - 15 working days
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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, 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
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