Titlebook: Outlier Detection for Temporal Data; Manish Gupta,Jing Gao,Jiawei Han Book 2014 Springer Nature Switzerland AG 2014

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Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Hanter examines the internal enabling environments for training delivery, laying a good foundation for the skills utilisation. In this connection, the findings revealed that the training delivery mechanism had a close link with the world of work in order to facilitate the utilisation of acquired skills

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e of film phenomenology. In doing so, it advances the field of VR phenomenology. Recognising that film phenomenology has a long tradition in film studies and philosophy, the key purpose of the chapter is to identify the specific aspects of the methodology that can be fully adopted, or adapted, for a

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2151-0067 cs, 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-dimen

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Book 2014ining, 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 dat

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Conclusions and Research Directions,, and briefly introduced the corresponding techniques. Finally, we also discussed various applications for which these techniques have been successfully used. Tis book provides a number of insights and lessons as follows.