SHADE: Deep Density-based Clustering

TL;DR

SHADE is a deep clustering algorithm that effectively detects arbitrarily shaped, density-connected clusters in high-dimensional noisy data, automatically identifying noise and providing interpretable visualizations.

Contribution

It introduces a novel loss function that incorporates density-connectivity into deep clustering, improving detection of complex cluster shapes without user input.

Findings

Outperforms existing methods in clustering quality on complex data

Automatically detects noise points without user intervention

Preserves cluster shapes for visualization and interpretation

Abstract

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. SHADE thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved.