An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering

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 cluster-ing 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 dif-ferent methods to provide suggestions for the selection of appro-priate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the cluster-ing 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 classifica-tion can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns.