Performance Evaluation of Clustering Algorithms on Trajectories Data
Clustering is a data mining technique used to place data elements into related groups without advance knowledge of the group definitions. In this paper, trajectories data are use to evaluate the performance of clustering algorithms on the factor of time parameter. The authors propose the time-based clustering algorithm that adapts the agglomerative and DBSCAN clustering algorithms for trajectory data. They present experimental results that show the performance and accuracy of clustering algorithms. Data mining can be performed on data represented in quantitative, textual, or multimedia forms.