5. Advanced Data Mining & Knowledge Engineering
Ontologies and Semantic Knowledge Graphs, Feature Engineering, Dimensionality Reduction, and Frequent Pattern Mining.
Technical Articles
01
Data Ontologies & Knowledge Graphs: RDF Triples, OWL & SPARQL Querying
RDF Subject-Predicate-Object triples, OWL Description Logic, TransE graph embeddings, and SPARQL queries.
02
Advanced Feature Engineering: Power Transformations, Target Encoding & Wavelets
Yeo-Johnson power transformations, Smoothed Out-Of-Fold (OOF) Target Encoding, and Wavelet features.
03
Manifold Learning & Dimensionality Reduction: PCA, t-SNE & UMAP Theory
PCA Spectral eigenvalue derivation, t-SNE Student-t KL optimization, and UMAP Fuzzy Simplicial Sets.
04
Frequent Pattern Mining: Apriori Bounds, FP-Growth & PrefixSpan
Association rule algebra (Support, Confidence, Lift), Apriori Downward Closure, and FP-Growth prefix trees.
05
Structural Anomaly Discovery: One-Class SVMs & Autoencoder Reconstruction
One-Class SVM hypersphere formulation, Autoencoder bottleneck reconstruction loss, and EVT POT thresholding.