Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios
This work addresses clustering challenges in scientific and engineering domains by providing a more adaptable method, though it appears incremental as it builds on existing data depth concepts.
The paper tackled the problem of clustering data with varying shapes and multimodal characteristics by proposing depth-based local center clustering (DLCC), which uses local data depth to identify clusters and a new internal metric for evaluation, resulting in a flexible approach that overcomes limitations of traditional methods.
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.