Clustering via Boundary Erosion

TL;DR

Clustering via Boundary Erosion introduces a novel method that identifies arbitrarily shaped clusters by sequentially eroding samples based on boundary density, effectively revealing cluster boundaries in complex data.

Contribution

The paper proposes a new boundary erosion-based clustering algorithm that automatically detects clusters of arbitrary shapes by sequentially eroding samples from low-density boundaries inward.

Findings

Outperforms most state-of-the-art clustering algorithms.

Achieves nearly perfect results in some scenarios.

Effectively detects arbitrarily shaped clusters.

Abstract

Clustering analysis identifies samples as groups based on either their mutual closeness or homogeneity. In order to detect clusters in arbitrary shapes, a novel and generic solution based on boundary erosion is proposed. The clusters are assumed to be separated by relatively sparse regions. The samples are eroded sequentially according to their dynamic boundary densities. The erosion starts from low density regions, invading inwards, until all the samples are eroded out. By this manner, boundaries between different clusters become more and more apparent. It therefore offers a natural and powerful way to separate the clusters when the boundaries between them are hard to be drawn at once. With the sequential order of being eroded, the sequential boundary levels are produced, following which the clusters in arbitrary shapes are automatically reconstructed. As demonstrated across various clustering tasks, it is able to outperform most of the state-of-the-art algorithms and its performance is nearly perfect in some scenarios.