MinUS: Mining User Similarity with Trajectory Patterns
Abstract
The development of positioning systems and wireless connectivity has made it possible to collect users’ fine-grained movement data. This availability of movement data can be applied in a broad range of services. In this paper, we present a novel tool for calculating users’ similarity based on their movements. This tool, MinUS , integrates the technologies of trajectory pattern mining with the state-of-the-art research on discovering user similarity with trajectory patterns. Specifically, with MinUS , we provide a platform to manage movement datasets, and construct and compare users’ trajectory patterns. Tool users can compare results given by a series of user similarity metrics, which allows them to learn the importance and limitations of different similarity metrics and promotes studies in related areas, e.g., location privacy. Additionally, MinUS can also be used by researchers as a tool for preliminary process of movement data and parameter tuning in trajectory pattern mining.
Cite
Text
Chen et al. "MinUS: Mining User Similarity with Trajectory Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_29Markdown
[Chen et al. "MinUS: Mining User Similarity with Trajectory Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/chen2014ecmlpkdd-minus/) doi:10.1007/978-3-662-44845-8_29BibTeX
@inproceedings{chen2014ecmlpkdd-minus,
title = {{MinUS: Mining User Similarity with Trajectory Patterns}},
author = {Chen, Xihui and Kordy, Piotr and Lu, Ruipeng and Pang, Jun},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {436-439},
doi = {10.1007/978-3-662-44845-8_29},
url = {https://mlanthology.org/ecmlpkdd/2014/chen2014ecmlpkdd-minus/}
}