Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping

Iryna Ivchenko; Oleh Ivchenko

doi:10.5281/zenodo.18701938

February 19, 2026Open Access

Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping

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Key Points

The aim is to analyze and categorize density-based clustering methods, particularly DBSCAN and OPTICS, highlighting their unique attributes and applications.
Reviewed existing literature on density-based clustering techniques.
Classified clustering algorithms based on their shape-awareness and density criteria.
Analyzed performance metrics in various datasets for DBSCAN and OPTICS.
DBSCAN and OPTICS exhibit distinct strengths in processing differently shaped datasets.
The taxonomy created helps clarify the use cases for each algorithm.
Findings reveal that shape-aware clustering can enhance data grouping accuracy.

Implication

This research article explores density-based clustering algorithms, emphasizing their classification and applications, indicating their relevance in data analysis.

Abstract

Research article: Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping

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Cite This Study

Ivchenko et al. (2026) studied this question.

synapsesocial.com/papers/69994cb3873532290d021593 https://doi.org/10.5281/zenodo.18701938

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Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping

Key Points

Implication

Abstract

Discussion

Cite This Study

Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping

Key Points

Implication

Abstract

Discussion

Cite This Study

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