A Qualitative Evaluation of Distance Measures in Trajectory Data Clustering

Trajectory clustering is one of the most important data mining tasks on this spatio-temporal data type. Many existing algorithms perform clustering by employing a distance measure designed specifically for trajectory data. However, compared to the clustering of n-dimensional points, the choice of a suitable distance measure for trajectory data is not straightforward. In this work, we conduct an experimental evaluation to examine the efficacy of different trajectory distance measures in relation to different data sets and different clustering algorithms. Our experiments show noticeable trends in the distribution of distance measure performance, dependent on clustering method and data set characteristics.