A Comprehensive Approach to Mode Clustering
This work provides incremental improvements to mode clustering for researchers and practitioners in data analysis.
The paper tackled the problem of improving mode clustering by introducing multiple enhancements, including soft cluster assignment and bandwidth selection, resulting in a complete procedure for multivariate clustering with comparisons to other methods.
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