Name: Hierarchical Taxonomy Clustering
Author: benchflow-ai

Hierarchical Taxonomy Clustering

Build unified multi-level category taxonomy from hierarchical product category paths from any e-commerce companies using embedding-based recursive clustering with intelligent category naming via weighted word frequency analysis.

benchflow-ai990 estrellas24 ene 2026

Ocupación
Categorías: Desarrollo E-commerce

Create a unified multi-level taxonomy from hierarchical category paths by clustering similar paths and automatically generating meaningful category names.

Problem

Given category paths from multiple sources (e.g., "electronics -> computers -> laptops"), create a unified taxonomy that groups similar paths across sources, generates meaningful category names, and produces a clean N-level hierarchy (typically 5 levels). The unified category taxonomy could be used to do analysis or metric tracking on products from different platform.

Methodology

Hierarchical Weighting: Convert paths to embeddings with exponentially decaying weights (Level i gets weight 0.6^(i-1)) to signify the importance of category granularity
Recursive Clustering: Hierarchically cluster at each level (10-20 clusters at L1, 3-20 at L2-L5) using cosine distance
Intelligent Naming: Generate category names via weighted word frequency + lemmatization + bundle word logic
Quality Control: Exclude all ancestor words (parent, grandparent, etc.), avoid ancestor path duplicates, clean special characters

Create a unified multi-level taxonomy from hierarchical category paths by clustering similar paths and automatically generating meaningful category names.

Problem

Methodology

Hierarchical Weighting: Convert paths to embeddings with exponentially decaying weights (Level i gets weight 0.6^(i-1)) to signify the importance of category granularity
Recursive Clustering: Hierarchically cluster at each level (10-20 clusters at L1, 3-20 at L2-L5) using cosine distance
Intelligent Naming: Generate category names via weighted word frequency + lemmatization + bundle word logic
Quality Control: Exclude all ancestor words (parent, grandparent, etc.), avoid ancestor path duplicates, clean special characters

Hierarchical Taxonomy Clustering

Problem

Methodology

Hierarchical Taxonomy Clustering

Problem

Methodology

Output

Installation

4-Step Pipeline

Step 1: Load, Standardize, Filter and Merge (`step1_preprocessing_and_merge.py`)

Step 2: Weighted Embeddings (`step2_weighted_embedding_generation.py`)

Step 3: Recursive Clustering (`step3_recursive_clustering_naming.py`)

Step 4: Export Results (`step4_result_assignments.py`)

Usage

Ordercli

Paypal Integration

Stripe Integration

Billing Automation

Odoo Ecommerce Configurator

Odoo Shopify Integration

Hierarchical Taxonomy Clustering

Problem

Methodology

Hierarchical Taxonomy Clustering

Problem

Methodology

Output

Installation

4-Step Pipeline

Step 1: Load, Standardize, Filter and Merge (step1_preprocessing_and_merge.py)

Step 2: Weighted Embeddings (step2_weighted_embedding_generation.py)

Step 3: Recursive Clustering (step3_recursive_clustering_naming.py)

Step 4: Export Results (step4_result_assignments.py)

Usage

Ordercli

Paypal Integration

Stripe Integration

Billing Automation

Odoo Ecommerce Configurator

Odoo Shopify Integration

Step 1: Load, Standardize, Filter and Merge (`step1_preprocessing_and_merge.py`)

Step 2: Weighted Embeddings (`step2_weighted_embedding_generation.py`)

Step 3: Recursive Clustering (`step3_recursive_clustering_naming.py`)

Step 4: Export Results (`step4_result_assignments.py`)