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Outlier detection for temporal data

Outlier detection for temporal data

Aggarwal, Charu C., Gao, Jing, Gupta, Manish, Han, Jiawei
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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.
Abstract: Compared to general outlier detection, techniques for temporal outlier detection are very different. This book presents an organised picture of both recent and past research in temporal outlier detection. It starts with the basics before moving on to the main ideas in state-of-the-art outlier detection techniques.
카테고리:
년:
2014
출판사:
Morgan & Claypool Publishers
언어:
english
페이지:
110
ISBN 10:
162705376X
ISBN 13:
9781627053761
시리즈:
Synthesis lectures on data mining and knowledge discovery #8
파일:
PDF, 9.64 MB
IPFS:
CID , CID Blake2b
english, 2014
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