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.