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Space Time Series Clustering: Algorithms, Taxonomy, and Case Study on Urban Smart Cities

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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03036868
Contributor : Florent Masseglia <>
Submitted on : Wednesday, December 2, 2020 - 8:01:17 PM
Last modification on : Tuesday, February 23, 2021 - 7:24:06 PM
Long-term archiving on: : Wednesday, March 3, 2021 - 8:16:38 PM

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Asma Belhadi, Youcef Djenouri, Kjetil Nørvåg, Heri Ramampiaro, Florent Masseglia, et al.. Space Time Series Clustering: Algorithms, Taxonomy, and Case Study on Urban Smart Cities. Engineering Applications of Artificial Intelligence, Elsevier, 2020, 95, pp.#103857. ⟨10.1016/j.engappai.2020.103857⟩. ⟨lirmm-03036868⟩

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