hierarchical-taxonomy-clustering

To unify product taxonomy from multiple e-commerce sources, this approach applies embedding-weighted clustering to group disparate hierarchical category paths into a single, consistent N-level taxonomy with meaningful names and a clean hierarchy.

The best way to cluster hierarchical category paths using embeddings involves a four-step pipeline: preprocessing paths, generating weighted embeddings, applying recursive clustering with cosine distance, and exporting the unified taxonomy.

Recursive clustering generates human-readable category names by combining weighted terms from the hierarchical paths and applying lemmatization through nltk, ensuring the final taxonomy labels are coherent and meaningful.

Yes, this taxonomy clustering approach works with pandas and sentence-transformers, alongside dependencies including numpy, scipy, nltk, and tqdm, to process category paths and generate embedding-weighted representations.

To handle duplicate categories and ancestor path collisions in a unified taxonomy, the pipeline applies quality control measures that remove duplicates and prevent collisions, ensuring stable taxonomy across datasets.

You need to build a fixed-depth hierarchy for cross-platform analytics when harmonizing vendor catalogs or enabling downstream search tasks, requiring a consistent N-level taxonomy (typically five levels) for accurate comparisons.