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Artificial Intelligence Review

Erschienen in:

15.07.2019

Spatiotemporal clustering: a review

verfasst von: Mohd Yousuf Ansari, Amir Ahmad, Shehroz S. Khan, Gopal Bhushan, Mainuddin

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2020

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Abstract

An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology studies, and mobile data analysis. This review paper presents a comprehensive review of spatiotemporal clustering approaches and their applications as well as a brief tutorial on the taxonomy of data types in the spatiotemporal domain and patterns. Additionally, the data pre-processing techniques, access methods, cluster validation, space–time scan statistics, software tools, and datasets used by various spatiotemporal clustering algorithms are highlighted.

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Titel: Spatiotemporal clustering: a review
verfasst von: Mohd Yousuf Ansari
Amir Ahmad
Shehroz S. Khan
Gopal Bhushan
Mainuddin
Publikationsdatum: 15.07.2019
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-019-09736-1

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