Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Abstract
We investigate the problem of hierarchically clustering data streams containing metric data in R^d. We introduce a desirable invariance property for such algorithms, describe a general family of hyperplane-based methods enjoying this property, and analyze two scalable instances of this general family against recently popularized similarity/dissimilarity-based metrics for hierarchical clustering. We prove a number of new results related to the approximation ratios of these algorithms, improving in various ways over the literature on this subject. Finally, since our algorithms are principled but also very practical, we carry out an experimental comparison on both synthetic and real-world datasets showing competitive results against known baselines.
Cite
Text
Rajagopalan et al. "Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees." International Conference on Machine Learning, 2021.Markdown
[Rajagopalan et al. "Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/rajagopalan2021icml-hierarchical/)BibTeX
@inproceedings{rajagopalan2021icml-hierarchical,
title = {{Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees}},
author = {Rajagopalan, Anand and Vitale, Fabio and Vainstein, Danny and Citovsky, Gui and Procopiuc, Cecilia M and Gentile, Claudio},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {8799-8809},
volume = {139},
url = {https://mlanthology.org/icml/2021/rajagopalan2021icml-hierarchical/}
}