Taxonomy-Driven Lumping for Sequence Mining
Abstract
In many application domains, events are naturally organized in a hierarchy. Whether events describe human activities, system failures, coordinates in a trajectory, or biomedical phenomena, there is often a taxonomy that should be taken into consideration. A taxonomy allow us to represent the information at a more general description level, if we choose carefully the most suitable level of granularity. Given a taxonomy of events and a dataset of sequences of these events, we study the problem of finding efficient and effective ways to produce a compact representation of the sequences. This can be valuable by itself, or can be used to help solving other problems, such as clustering.
Cite
Text
Bonchi et al. "Taxonomy-Driven Lumping for Sequence Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_14Markdown
[Bonchi et al. "Taxonomy-Driven Lumping for Sequence Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/bonchi2009ecmlpkdd-taxonomydriven/) doi:10.1007/978-3-642-04180-8_14BibTeX
@inproceedings{bonchi2009ecmlpkdd-taxonomydriven,
title = {{Taxonomy-Driven Lumping for Sequence Mining}},
author = {Bonchi, Francesco and Castillo, Carlos and Donato, Debora and Gionis, Aristides},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {29},
doi = {10.1007/978-3-642-04180-8_14},
url = {https://mlanthology.org/ecmlpkdd/2009/bonchi2009ecmlpkdd-taxonomydriven/}
}