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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Detalles Bibliográficos

Main Authors:	Kovacevic, Ana, Urosevic, Vladimir, Kaddachi, Firas
Formato:	Online
Idioma:	inglés
Publicado:	InTechOpen 2021
Subjects:	temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
Acceso en liña:	ONIX_20210602_10.5772/intechopen.75203_392
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