A comprehensive approach to mode clustering
Copyright policy )A comprehensive approach to mode clustering
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
34 pages, 17 figures. Accepted to the Electronic Journal of Statistics. The original title is "Enhanced Mode Clustering"
nonparametric clustering, FOS: Computer and information sciences, Kernel density estimation, Classification and discrimination; cluster analysis (statistical aspects), Machine Learning (stat.ML), Nonparametric inference, mean shift clustering, Methodology (stat.ME), Density estimation, soft clustering, Statistics - Machine Learning, 62H30 (Primary), 62G07, 62G99 (Secondary), 62G07, kernel density estimation, 62G99, 62H30, visualization, Statistics - Methodology
nonparametric clustering, FOS: Computer and information sciences, Kernel density estimation, Classification and discrimination; cluster analysis (statistical aspects), Machine Learning (stat.ML), Nonparametric inference, mean shift clustering, Methodology (stat.ME), Density estimation, soft clustering, Statistics - Machine Learning, 62H30 (Primary), 62G07, 62G99 (Secondary), 62G07, kernel density estimation, 62G99, 62H30, visualization, Statistics - Methodology
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