Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios

TL;DR

This paper introduces depth-based local center clustering (DLCC), a flexible method that uses local data depth to identify clusters of various shapes, addressing limitations of traditional clustering techniques.

Contribution

The paper presents a novel DLCC method that employs local data depth for effective clustering of complex, multimodal, and non-convex data distributions.

Findings

DLCC effectively identifies clusters of varying shapes.

DLCC outperforms traditional methods on non-convex data.

A new internal metric evaluates clustering quality in complex scenarios.

Abstract

Cluster analysis, or clustering, plays a crucial role across numerous scientific and engineering domains. Despite the wealth of clustering methods proposed over the past decades, each method is typically designed for specific scenarios and presents certain limitations in practical applications. In this paper, we propose depth-based local center clustering (DLCC). This novel method makes use of data depth, which is known to produce a center-outward ordering of sample points in a multivariate space. However, data depth typically fails to capture the multimodal characteristics of {data}, something of the utmost importance in the context of clustering. To overcome this, DLCC makes use of a local version of data depth that is based on subsets of {data}. From this, local centers can be identified as well as clusters of varying shapes. Furthermore, we propose a new internal metric based on density-based clustering to evaluate clustering performance on {non-convex clusters}. Overall, DLCC is a flexible clustering approach that seems to overcome some limitations of traditional clustering methods, thereby enhancing data analysis capabilities across a wide range of application scenarios.