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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Kovacevic, Ana, Urosevic, Vladimir, Kaddachi, Firas
Aineistotyyppi:	Online
Kieli:	englanti
Julkaistu:	InTechOpen 2021
Aiheet:	temporal clustering, IoT, smart cities, behavior recognition, anomaly detection thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
Linkit:	ONIX_20210602_10.5772/intechopen.75203_392
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