Abstract
This paper provides a short overview of space–time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clustering. The first hierarchical category is to identify hierarchies in space–time series data. The second partitioning-based category focuses on determining disjoint partitions among the space–time series data, whereas the third overlapping category explores fuzzy logic to determine the different correlations between the space–time series clusters. We also further describe solutions for each category in this paper. Furthermore, we show the applications of these solutions in an urban traffic data captured on two urban smart cities (e.g., Odense in Denmark and Beijing in China). The perspectives on open questions and research challenges are also mentioned and discussed that allow to obtain a better understanding of the intuition, limitations, and benefits for the various space–time series clustering methods. This work can thus provide the guidances to practitioners for selecting the most suitable methods for their used cases, domains, and applications.
Highlights
Recent advances in geolocation, partly as a result of GPS (Global Positioning System) support, has resulted in the creation of large volumes of data varied in time and space
We can observe that the k-means and DBSCAN are the most powerful methods compared to the other space–time series clustering algorithms
The results revealed that the classification ratio of the space–time series clustering algorithms is decreased while increasing the number of traffic flow values
Summary
Partly as a result of GPS (Global Positioning System) support, has resulted in the creation of large volumes of data varied in time and space. This paper presents a comprehensive overview of the existing space– time series clustering algorithms. The first category is called hierarchical space–time series clustering that is used to create hierarchical clusters among the space–time series data. The second category is named pure partitioning space– time series clustering that is utilized to partition the space–time series into disjoint and similar clusters. We show the applications of existing space–time series clustering on urban traffic data relevant to two smart cities (e.g., Odense in Denmark and Beijing in China). Compared to previous survey papers, this paper first provides a deep analysis of space–time series clustering techniques, which allows to clearly understand the merits and the limits of the reviewed algorithms for each space–time series clustering category. Paper derives mature solutions for space–time series clustering, in particular for massive data, and for emerging applications
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