Chapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities

In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and d...

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Bibliografski detalji
Glavni autori: Kovacevic, Ana, Urosevic, Vladimir, Kaddachi, Firas
Format: Livro
Jezik:Inglês
Izdano: InTechOpen 2018
Teme:
temporal clustering, IoT, smart cities, behavior recognition, anomaly detection
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
Online pristup:https://directory.doabooks.org/handle/20.500.12854/70489
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