Summary. 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. Geographic and temporal properties are a key aspect of many data analysis problems in business, government, and science. Through the avaiIabiIity of cheap sensor devices we have witnessed an exponential growth of geo-tagged data in the last few years resulting in the availability of fine-grained geographic data at small temporal sampling intervals. Therefore, the actual chaIlenge in geo-temporal analysis is moving from acquiring the right data towards large-scale analysis of the available data. Clustering is one approach to analyze geo-temporal data at a higher level of abstraction by grouping the data according to its similarity into meaningful clusters. While the two dimensional geographie dimensions are relatively manageable, their combination with time results in a number of chaIlenges. It is mostly application dependent how the weight of the time dimension should be considered in a distance metric. When tracking pedestrians, for example, two geographically c10se sampIe points co-occurring within aminute interval could belong to the same cluster, whereas two sampIe points at near distance within a time interval of a few nanoseconds in a physies experiment might belong to different clusters. In addition to this, representing temporal information on a map becomes extremely challenging. When considering a group of points in time as a single entity, more complex data types such as trajectories emerge. Analysis questions might then deal with the correlation of these
In fact, the work in REF classifies spatial-temporal data based on the relevance of its spatial and temporal dimensions, and its shape.
Spatio-temporal clustering
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Geography Computer Science