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The Fundamental Clustering and Projection Suite (FCPS): A Dataset Collection to Test the Performance of Clustering and Data Projection Algorithms

https://doi.org/10.3390/data5010013
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/nav.3800080314openalex-snapshot
Adaptive control processes—A guided tour, by Richard Bellman, Princeton University Press, Princeton, New Jersey, 1961, 255 pp., $6.50
inferredcites →https://doi.org/10.1016/j.jbi.2016.12.011openalex-snapshot
Machine-learned cluster identification in high-dimensional data
inferredcites →https://doi.org/10.1023/a:1007662407062openalex-snapshot
Large Margin Classification Using the Perceptron Algorithm
inferredcites →https://doi.org/10.1080/01621459.1963.10500845openalex-snapshot
Hierarchical Grouping to Optimize an Objective Function
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LIII. On lines and planes of closest fit to systems of points in space
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FactoMineR : An R Package for Multivariate Analysis
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A Comparison of Some Methods of Cluster Analysis
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ANN2: Artificial Neural Networks for Anomaly Detection
inferredcites →https://doi.org/10.32614/cran.package.rtsneopenalex-snapshot
Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation
inferredcites →https://doi.org/10.3390/ijms21010079openalex-snapshot
Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data
inferredcites →https://doi.org/10.4169/college.math.j.46.3.178openalex-snapshot
On the Shrinking Volume of the Hypersphere
inferredcites →https://doi.org/10.5555/2627435.2697068openalex-snapshot
Accelerating t-SNE using tree-based algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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How to Impute If You Must: A Data Science Method for Selecting the Missing Value Imputation Strategy for Cross-Sectional Biomedical Numerical Data
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Comparative Assessment of Projection and Clustering Method Combinations in the Analysis of Biomedical Data
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Interpretation of cluster structures in pain‐related phenotype data using explainable artificial intelligence (XAI)
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Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data
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Interpretable Clustering via Soft Clustering Trees
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Comparative assessment of projection and clustering method combinations in the analysis of biomedical data
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Dimensionality-modulated generative AI for safe biomedical dataset augmentation
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Selected confidence sample labeling for domain adaptation
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A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data
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Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks
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AeneasHDC: An Automatic Framework for Deploying Hyperdimensional Computing Models on FPGAs
inferred← citeshttps://doi.org/10.1109/tc.2022.3179226openalex-snapshot
OpenHD: A GPU-Powered Framework for Hyperdimensional Computing
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Secure and Efficient L p -Norm Computation for Two-Party Learning Applications
inferred← citeshttps://doi.org/10.1186/s12859-022-04769-wopenalex-snapshot
Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans)
inferred← citeshttps://doi.org/10.1186/s12920-025-02154-zopenalex-snapshot
Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry
inferred← citeshttps://doi.org/10.1371/journal.pone.0333653openalex-snapshot
Voronoi tessellation as a complement or replacement for confidence ellipses in the visualization of data projection and clustering results
inferred← citeshttps://doi.org/10.1609/aaai.v36i7.20693openalex-snapshot
Interpretable Clustering via Multi-Polytope Machines
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2658032/v1openalex-snapshot
Comparative assessment of projection and clustering method combinations in the analysis of biomedical data
inferred← citeshttps://doi.org/10.2139/ssrn.5366515openalex-snapshot
Mitigating Error Propagation in Generative AI via Dimensionality Modulation as a Method for Safe Biomedical Dataset Augmentation
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When Artifacts Masquerade as Discovery: A Case Study Revealing Hidden Laboratory Errors in Lipidomics Data with Biologically Apparently Plausible Results
inferred← citeshttps://doi.org/10.24963/ijcai.2023/225openalex-snapshot
Optimal Decision Trees For Interpretable Clustering with Constraints
inferred← citeshttps://doi.org/10.3390/biomedinformatics3040054openalex-snapshot
Pitfalls of Using Multinomial Regression Analysis to Identify Class-Structure-Relevant Variables in Biomedical Data Sets: Why a Mixture of Experts (MOE) Approach Is Better
inferred← citeshttps://doi.org/10.5753/ercemapi.2021.17911openalex-snapshot
Um Estudo Comparativo entre Algoritmos de Agrupamentos de Dados Usando a Ferramenta YADMT
inferred← citeshttps://doi.org/10.6084/m9.figshare.20088140openalex-snapshot
Additional file 1 of Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans)
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