# Hierarchical Taxonomy Clustering

Build unified multi-level category taxonomy from hierarchical product category paths...

- Repository: https://github.com/benchflow-ai/skillsbench (1888 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-24
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add benchflow-ai/skillsbench --skill hierarchical-taxonomy-clustering -g -a claude-code -y`
Codex: `npx skills add benchflow-ai/skillsbench --skill hierarchical-taxonomy-clustering -g -a codex -y`
Cursor: `npx skills add benchflow-ai/skillsbench --skill hierarchical-taxonomy-clustering -g -a cursor -y`
Gemini CLI: `npx skills add benchflow-ai/skillsbench --skill hierarchical-taxonomy-clustering -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Hierarchical Taxonomy Clustering

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

1. **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
2. **Recursive Clustering**: Hierarchically cluster at each level (10-20 clusters at L1, 3-20 at L2-L5) using cosine distance
3. **Intelligent Naming**: Generate category names via weighted word frequency + lemmatization + bundle word logic
4. **Quality Control**: Exclude all ancestor words (parent, grandparent, etc.), avoid ancestor path duplicates, clean special characters

## Output

DataFrame with added columns:
- `unified_level_1`: Top-level category (e.g., "electronic | device")
- `unified_level_2`: Second-level category (e.g., "computer | laptop")
- `unified_level_3` through `unified_level_N`: Deeper levels

Category names use ` | ` separator, max 5 words, covering 70%+ of records in each cluster.

## Installation

```bash
pip install pandas numpy scipy sentence-transformers nltk tqdm
python -c "import nltk; nltk.download('wordnet'); nltk.download('omw-1.4')"
```

## 4-Step Pipeline

The rest: https://github.com/benchflow-ai/skillsbench/blob/HEAD/tasks-extra/taxonomy-tree-merge/environment/skills/hierarchical-taxonomy-clustering/SKILL.md