TY - JOUR
T1 - An overview of clustering methods for geo-referenced time series
T2 - from one-way clustering to co- and tri-clustering
AU - Wu, Xiaojing
AU - Cheng, Changxiu
AU - Zurita-Milla, R.
AU - Song, Changqing
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns.
AB - Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - Spatio-temporal pattern
KW - method selection
KW - data mining
KW - classification
KW - clustering analysis
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1080/13658816.2020.1726922
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2020/isi/zuritamilla_ove.pdf
UR - http://www.scopus.com/inward/record.url?scp=85079730107&partnerID=8YFLogxK
U2 - 10.1080/13658816.2020.1726922
DO - 10.1080/13658816.2020.1726922
M3 - Review article
VL - 34
SP - 1822
EP - 1848
JO - International journal of geographical information science
JF - International journal of geographical information science
SN - 1365-8816
IS - 9
ER -