Utilizing Structure-Rich Features to Improve Clustering
Abstract
For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlapping and thus the boundaries clearly defined, features where the clusters blend into each other hinder clustering methods to correctly estimate these boundaries. Therefore, we aim to extract features showing clear cluster boundaries and thus enhance the cluster structure in the data. Our novel technique creates a condensed version of the data set containing the structure important for clustering, but without the noise-information. We demonstrate that this transformation of the data set is much easier to cluster for k-means, but also various other algorithms. Furthermore, we introduce a deterministic initialisation strategy for k-means based on these structure-rich features.
Cite
Text
Schelling et al. "Utilizing Structure-Rich Features to Improve Clustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67658-2_6Markdown
[Schelling et al. "Utilizing Structure-Rich Features to Improve Clustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/schelling2020ecmlpkdd-utilizing/) doi:10.1007/978-3-030-67658-2_6BibTeX
@inproceedings{schelling2020ecmlpkdd-utilizing,
title = {{Utilizing Structure-Rich Features to Improve Clustering}},
author = {Schelling, Benjamin and Bauer, Lena Greta Marie and Behzadi, Sahar and Plant, Claudia},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {91-107},
doi = {10.1007/978-3-030-67658-2_6},
url = {https://mlanthology.org/ecmlpkdd/2020/schelling2020ecmlpkdd-utilizing/}
}