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

2025-05-14

AI Summary

Existing clustering methods (e.g., K-means, DBSCAN) struggle with multimodal, non-convex, nested, and noisy data, often relying on predefined parameters or global structural assumptions. To address these limitations, we propose Deep-driven Local Centroid Clustering (DLCC). DLCC introduces the novel concept of *local data depth*, overcoming the inadequacy of global depth in characterizing multimodal structures; designs a density-sensitive intra-cluster validity metric to assess internal quality of non-convex clusters; and incorporates subset-based depth ranking with a parameter-free, adaptive mechanism for local centroid identification. Extensive experiments demonstrate that DLCC significantly outperforms state-of-the-art methods on datasets featuring diverse shapes, non-convexity, nesting, and noise. Crucially, DLCC requires no prior specification of the number of clusters, exhibits strong generalizability, and maintains robustness and practical utility across heterogeneous scenarios.

Technology Category

• Machine Learning: Clustering
• Search and Optimization: Local Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Handling diverse clustering scenarios with limitations
• Capturing multimodal data characteristics effectively
• Evaluating performance on non-convex clusters

Innovation

• Uses local data depth for multimodal clustering
• Identifies local centers and varying shape clusters
• Introduces density-based metric for non-convex evaluation