Global distance-based segmentation of trajectories

This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primitives that are better suited for storage and retrieval purposes. Previous work on trajectory segmentation attacked the problem locally, segmenting separately each trajectory of the database. Therefore, they did not directly optimize the inter-object separability, which is necessary for mining operations such as searching, clustering, and classification on large databases. In this paper we analyze the trajectory segmentation problem from a global perspective, utilizing data aware distance-based optimization techniques, which optimize pairwise distance estimates hence leading to more efficient object pruning. We first derive exact solutions of the distance-based formulation. Due to the intractable complexity of the exact solution, we present an approximate, greedy solution that exploits forward searching of loc...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu
• Keywords: Data Mining, Distance-based Optimization Techniques, KDD 2006, Trajectory Segmentation Problem, Variance-based Segmentation Techniques