Spatio-temporal clustering

Abstract

Spatio-temporal c1ustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographie information scienees due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal c1ustering in geographic space. First, we provide a c1assification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal c1ustering -trajectory c1ustering, provide an overview of the state-of-the-art approach es and methods of spatio-temporal c1ustering and finally present several scenarios in different application domains such as movement, ceIIular networks and environmental studies.

Long-Term Marine Traffic Monitoring for Environmental Safety in the Aegean Sea

“…This is the task of grouping objects based on their spatial and temporal similarity (Kisilevich et al, 2010). The goal of this submodule is therefore to group similar vessel trajectories into homogeneous categories.…”

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

“…Kisilevich et al [35] describe state-of-the-art approaches of clustering for geospatial data. Guo [26,27] applies spatially constrained hierarchic graph partitioning techniques to group places into larger units and then visualizes aggregated flows between these larger units.…”

The Motif-Based Approach to the Analysis of the Employee Trajectories within Organization

“…When an analyst does not possess labeled data describing normal and abnormal behavior, the most widely used approach is to apply clustering-based techniques to investigate trajectories [11]. The obtained clusters are then used to describe normal behavior of the moving objects, while outliers can be used to detect anomalies.…”

“…The obtained clusters are then used to describe normal behavior of the moving objects, while outliers can be used to detect anomalies. The clusters may be found by centroid based approaches, hierarchical models, or density-based approaches [10][11][12][13][14]. In [15], authors construct a probability tree to mine movement patterns of the person movement inside the room.…”