(k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping
Milutin Brankovic, Kevin Buchin, Koen Klaren, André Nusser, Aleksandr Popov, Sampson Wong
Abstract
Due to the massively increasing amount of available geospatial data and the need to present it in an understandable way, clustering this data is more important than ever. As clusters might contain a large number of objects, having a representative for each cluster significantly facilitates understanding a clustering. Clustering methods relying on such representatives are called center-based. In this work we consider the problem of center-based clustering of trajectories.
Topics & Concepts
Cluster analysisComputer scienceData miningDynamic time warpingCorrelation clusteringCluster (spacecraft)Geospatial analysisCURE data clustering algorithmFuzzy clusteringArtificial intelligenceSingle-linkage clusteringData stream clusteringClustering high-dimensional dataFLAME clusteringPattern recognition (psychology)k-medians clusteringTrajectoryCanopy clustering algorithmFeature (linguistics)Key (lock)Data Management and AlgorithmsTime Series Analysis and ForecastingAdvanced Database Systems and Queries