@inbook{80c3d4263d6743559676b99ccbed1ebd,
title = "Measuring similarity between trajectories of mobile objects",
abstract = "With technological progress we encounter more available data on the locations of moving objects and therefore the need for mining moving objects data is constantly growing. Mining spatio-temporal data can direct products and services to the right customers at the right time; it can also be used for resources optimization or for understanding mobile patterns. In this chapter, we cluster trajectories in order to find movement patterns of mobile objects. We use a compact representation of a mobile trajectory, which is based on a list of minimal bounding boxes (MBBs). We introduce a new similarity measure between mobile trajectories and compare it empirically to an existing similarity measure by clustering spatio-temporal data and evaluating the quality of resulting clusters and the algorithm run times.",
author = "Sigal Elnekave and Mark Last and Oded Maimon",
year = "2008",
doi = "10.1007/978-3-540-76831-9_5",
language = "אנגלית",
isbn = "9783540768302",
series = "Studies in Computational Intelligence",
publisher = "Springer Berlin Heidelberg",
pages = "101--128",
editor = "Horst Bunke and Abraham Kandel and Mark Last",
booktitle = "Applied Pattern Recognition",
}