An Information Theory Based Approach to Multisource Clustering
Abstract
Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.
Cite
Text
Murena et al. "An Information Theory Based Approach to Multisource Clustering." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/358Markdown
[Murena et al. "An Information Theory Based Approach to Multisource Clustering." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/murena2018ijcai-information/) doi:10.24963/IJCAI.2018/358BibTeX
@inproceedings{murena2018ijcai-information,
title = {{An Information Theory Based Approach to Multisource Clustering}},
author = {Murena, Pierre-Alexandre and Sublime, Jérémie and Matei, Basarab and Cornuéjols, Antoine},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {2581-2587},
doi = {10.24963/IJCAI.2018/358},
url = {https://mlanthology.org/ijcai/2018/murena2018ijcai-information/}
}