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

手勢(shì)

存心

GRAVE

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

Trochlea

共同時(shí)代

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

盡忠

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

NORM

Abbreviate

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

保守

V洗浴

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.