Triangulation-Based Spatial Clustering for Adjacent Data With Heterogeneous Density.

Area of Science:

• Data Science
• Computational Geometry
• Spatial Analysis

Background:

• Traditional clustering algorithms struggle with complex datasets.
• Irregular cluster shapes, heterogeneous densities, and intricate domains pose challenges.
• Existing methods fail to effectively analyze nonlinear relationships and noisy boundaries.

Purpose of the Study:

• Introduce a novel Density and Triangulation-based Clustering (DTC) framework.
• Address limitations of traditional clustering in complex spatial domains.
• Enhance cluster identification for irregular, noisy, and heterogeneous data.

Main Methods:

• Advanced density estimation for complex domains.
• Delaunay triangulation for spatial clustering and nonlinear geometry management.
• Proximity analysis using nearest neighbors for noise mitigation.

Main Results:

• DTC successfully identifies nested and contiguous clusters.
• The framework handles heterogeneous densities and complex spatial domains effectively.
• Demonstrated superior performance on synthetic and real-world datasets compared to traditional algorithms.

Conclusions:

• The DTC framework offers a versatile solution for challenging clustering tasks.
• It excels in scenarios with irregular shapes, varying densities, and noise.
• DTC enables meaningful insight extraction from complex, intricate datasets.