References

This page lists the bibliographic and technical references used to document Core-SG, its HDBSCAN-style hierarchy behavior, Score-SG, approximate nearest neighbor construction, and noise-label post-processing.

Academic References

Core-SG

Antonio Cavalcante Araujo Neto, Murilo Coelho Naldi, Ricardo J. G. B. Campello, and Jörg Sander. CORE-SG: Efficient Computation of Multiple MSTs for Density-Based Methods. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 951–964, 2022. DOI: 10.1109/ICDE53745.2022.00076.

HDBSCAN and Hierarchical Density Clustering

Leland McInnes and John Healy. Accelerated Hierarchical Density Based Clustering. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 33–42, 2017. DOI: 10.1109/ICDMW.2017.12.

Ricardo J. G. B. Campello, Davoud Moulavi, and Jörg Sander. Density-Based Clustering Based on Hierarchical Density Estimates. In: Advances in Knowledge Discovery and Data Mining, Springer, pp. 160–172, 2013. DOI: 10.1007/978-3-642-37456-2_14.

Ricardo J. G. B. Campello, Davoud Moulavi, Arthur Zimek, and Jörg Sander. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), 2015. DOI: 10.1145/2733381.

Noise Handling and Density-Based Label Propagation

Jadson Castro Gertrudes, Arthur Zimek, Jörg Sander, and Ricardo J. G. B. Campello. A Unified View of Density-Based Methods for Semi-Supervised Clustering and Classification. Data Mining and Knowledge Discovery, 33, 1894–1952, 2019. DOI: 10.1007/s10618-019-00651-1.

Approximate Nearest Neighbors and PyNNDescent

Wei Dong, Moses Charikar, and Kai Li. Efficient k-Nearest Neighbor Graph Construction for Generic Similarity Measures. In: Proceedings of the 20th International Conference on World Wide Web (WWW), ACM, pp. 577–586, 2011. DOI: 10.1145/1963405.1963487.

Software and Documentation References

HDBSCAN

The Core-SG hierarchy adapter is documented against the HDBSCAN ecosystem and its private tree-processing internals.

HDBSCAN documentation: https://hdbscan.readthedocs.io/en/latest/
HDBSCAN repository: https://github.com/scikit-learn-contrib/hdbscan

PyNNDescent

Score-SG uses PyNNDescent for approximate nearest-neighbor graph construction.

PyNNDescent documentation: https://pynndescent.readthedocs.io/en/stable/
PyNNDescent package metadata: https://pypi.org/project/pynndescent/

Scikit-Learn

CoreSGClusterer follows the scikit-learn estimator style for fit, fit_predict, parameter introspection, and cloning behavior.

scikit-learn documentation: https://scikit-learn.org/stable/

Documentation Tooling

The GitHub Pages site is built with Sphinx and the Read the Docs theme.

Sphinx documentation: https://www.sphinx-doc.org/
sphinx-rtd-theme documentation: https://sphinx-rtd-theme.readthedocs.io/

Reference Usage Map

The Core-SG paper motivates the reusable support graph and repeated multi-k MST extraction workflow.
The HDBSCAN and hierarchical density clustering references motivate the hierarchy, condensed tree, persistence, and HDBSCAN-style output language.
The density-based semi-supervised clustering reference informs the current noise-label reassignment discussion.
The nearest-neighbor descent reference and PyNNDescent documentation inform the Score-SG approximate kNN construction discussion.
The scikit-learn documentation informs the estimator-oriented API presentation.