Why BIDistances? | Access to distance methods | Data and Distance conventions | One dispatcher for different distance families | Theory-guided comparison of distance distributions | Mathematical properties are explicit | Weighted Minkowski distances on CPU and OpenCL | Cross-backend agreement on the CPU | OpenCL route | Gene Ontology-derived TF-IDF distances | Time-series and mixed-data routes | Which entry point should be used? | References

Why memshare? | Install | 5‑minute tour | 1) Column-wise work on a matrix (memApply) | 2) List workloads (memLapply) | 3) Low-level control (register / retrieve / release) | Concepts that matter | Common patterns | Feature map over columns (fast and memory-light) | Multiple passes on the same data | Tips and best practices | Troubleshooting | Essentials | References

PDE Naive Bayes (PDENB) | Fitting a PDENB model | Visualization and interpretation | Plausible Bayes correction | References

Introduction | First Example: Automatic approach | First Module: Projection of high-dimensional Data | Second Module: Generalized Umatrix | Third Module: Automatic Clustering | First Module: Projection of High-dimensional Data | Third Module: Interactive Clustering | Generating the Shape of an Island out of the Topograpahic Map | Manually Improving the Clustering Using the Topograpahic Map | Quality Measures of Projection and Clustering | Gabriel Classification Error | Clustering Accuracy

Exploratory Data Analysis | Synoptic Overview | Distribution Analysis | Mirrored Density Plots (MD-plots) | Correlation Analysis | Distribution of Distances | Spatial Visualization - Choropleth Map | Spatial Visualization - World Map with classification | Visualization of categorical features | Evaluation of Clustering | Visualizations of Unsupervised Indices | Visualizations of Supervised Indices | References

Cluster Analysis with FCPS | Generating a Clustering Challenge | Testing for Cluster Tendency | Estimating the Number of Clusters | Accurate Comparison to a Given Ground Truth | Further Functionality | Summary

Introduction | Generate a Two-Dimensional Scatter Plot of High-Dimensional Data | Interpretation of Scatter Plot | Compute the Generalized Umatrix using sESOM | Interpretation of the Topographic Map | Saving Output | Generating the Shape of an Island out of the Topographic Map | Manually Clustering the Projection Using the Topographic Map

Gaussian Mixture Models (GMM) | Multimodal Natural Dataset not Suitable for a GMM | References