TY - JOUR
T1 - Spatiotemporal clustering
T2 - a review
AU - Ansari, Mohd Yousuf
AU - Ahmad, Amir
AU - Khan, Shehroz S.
AU - Bhushan, Gopal
AU - Mainuddin,
N1 - Funding Information:
This work is supported by Ministry of Electronics and Information Technology, Government of India under the Visvesvaraya Ph.D. scheme.
Publisher Copyright:
© 2019, Springer Nature B.V.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - Cluster validation
KW - Data mining
KW - Patterns
KW - Spatiotemporal clustering
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U2 - 10.1007/s10462-019-09736-1
DO - 10.1007/s10462-019-09736-1
M3 - Article
AN - SCOPUS:85068956432
SN - 0269-2821
VL - 53
SP - 2381
EP - 2423
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 4
ER -