DBSCAN
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Related Articles from SNS
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
arXiv:2605.30225v2 Announce Type: replace Abstract: Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable to clustering, making it challenging to understand cluster assignments. This interpretability gap is particularly evident in the popular density-based method DBSCAN, which assigns points as inliers (cluster members in dense regions) or outliers (noise points in...
Using protein language models for pangenome construction
Current pangenome construction methods rely largely on nucleotide or protein sequence alignment, limiting their ability to detect remote orthologs and semantic relations. We introduce a novel method that leverages protein language model embeddings to capture functional and semantic relationships beyond sequence similarity. Our approach employs approximate nearest-neighbor search coupled with a clustering step utilizing HDBSCAN, DBSCAN, or weighted single-linkage clustering with multiple...
Intra-slide calibration technology improves immunohistochemical harmonization within and between anatomic pathology laboratories
The reproducibility of immunohistochemistry in tumor tissue analysis across reference labs remains a persistent challenge. We tested the extent to which an intra-slide calibration technology mitigated discprepencies in inter-laboratory assays of p53 immunohistochemical (IHC) reactions in brain biopsies of glioblastoma (GB), IDH-wildtype. Intra-slide calibration technologies apply a 0-100% concentration scale incorporating primary surrogate and secondary antibodies to generate a standardized...
Central Description Length (CDL) Clustering Validation Index
arXiv:2606.05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data. Clustering validation indices (CVIs) provide internal scores for ranking candidate clusterings, but most popular CVIs are built from Euclidean compactness and separation terms and so tend to favour compact, convex partitions.