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

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Outlier Detection for Distributed Data Streams, deployed at various geographic locations and then data is often transmitted to central stations. Similarly, Twitter data can also be considered as distributed data obtained from human sensors spread all across the world. [Zhang et al., 2008] provide a detailed survey for outlier detection technique

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Outlier Detection for Temporal Network Data,tasets (often as relational databases), the networks (or graphs) can capture much more semantics using links between the tuples. Tough effective outlier detection techniques exist for multi-dimensional data, new techniques need to be designed for networks which present a different set of challenges.

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Conclusions and Research Directions,a challenging task due to the dynamic nature and complex evolutionary patterns in the data. A wide variety of models have been developed to capture different facets in temporal data outlier detection. Tis book organized the discussion along different data types, presented various outlier definitions

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Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Hans for investigating (smooth or nonsmooth) elliptic PDEs.Prov.This book provides a modern and comprehensive presentation of a wide variety of problems arising in nonlinear analysis, game theory, engineering, mathematical physics and contact mechanics. It includes recent achievements and puts them int

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