A Comprehensive Approach to Mode Clustering
Methodology
2015-12-23 v4 Machine Learning
Abstract
Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the bandwidth, (iv) a method for denoising small clusters, and (v) an approach to visualizing the clusters. Combining all these enhancements gives us a complete procedure for clustering in multivariate problems. We also compare mode clustering to other clustering methods in several examples
Cite
@article{arxiv.1406.1780,
title = {A Comprehensive Approach to Mode Clustering},
author = {Yen-Chi Chen and Christopher R. Genovese and Larry Wasserman},
journal= {arXiv preprint arXiv:1406.1780},
year = {2015}
}
Comments
34 pages, 17 figures. Accepted to the Electronic Journal of Statistics. The original title is "Enhanced Mode Clustering"