Robust Similarity Measures for Mobile Object Trajectories

We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such kind of data may contain a great amount of outliers, which degrades the performance of Euclidean and Time Warping Distance. Therefore, here we propose the use of non-metric distance functions based on the Longest Common Subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various ÄÔ Norms and also to Time Warping distance (for real and synthetic data) and we present experimental results that validate the accuracy and efficiency of our approach, especially under the strong presence of noise.

• Type: Conference
• Year: 2002
• Where: DEXAW
• Authors: Michail Vlachos, Dimitrios Gunopulos, George Kollios
• Keywords: Database, DEXAW 2002, Longest Common Subsequence, Non-metric Distance Functions, Time Warping Distance