Trajectory Cluster Lifecycle Analysis: An Evolutionary Perspective

Cluster analysis has helped to uncover changes over time in numerous studies on the dynamics of entities such as people and groups of animals in areas such as human mobility, health, transportation, commerce, and ecology. However, there is a lack of methods that focus on aspects related to the cluster lifecycle, including dynamic analyses on how clusters are formed, change, and disappear. Specifically, how objects enter and exit from the clusters, and how clusters are (de-)composed to form new clusters. In this paper, we introduce our work in progress about an approach to trajectory cluster lifecycle analysis based on big data that supports an evolutionary analysis of clusters throughout their lifecycle. The knowledge that can be captured as a result of such novel forms of analysis will advance the state of the art in a wide range of applications that require information about cluster evolution, and thus provide deeper insights on cluster genesis, existence, and disappearance.