nicodv/kmodes
Some data may be outdated — see provenance labels.
Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data observed · 2026-09-23
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4828
- days_rel: n/a
- days_push: 853
- n_releases_24m: 0
Adoption not part of the score
1285 stars · 411 forks observed · 2026-09-23
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python library implementing the k-modes and k-prototypes clustering algorithms for categorical and mixed numerical/categorical data. It follows the scikit-learn API and relies on numpy for performance.
Use cases
- cluster categorical survey responses
- group customers by categorical attributes
- cluster mixed numeric and categorical data
- segment data without one-hot encoding
- apply k-modes clustering in a scikit-learn pipeline
When to choose
- your dataset is mostly or entirely categorical features
- you want a scikit-learn-compatible clustering API
- you need k-prototypes for mixed data types
When to avoid
- your data is purely numerical and k-means suffices
- you need very large-scale distributed clustering
Facets
library · maturity stable
machine-learning data-science machine-learning data-science python clustering k-modes k-prototypes categorical-data scikit-learn unsupervised-learning
2 sources
- readme: https://github.com/nicodv/kmodes · fetched 2026-09-23 · 533578606269
- registry_pypi: https://pypi.org/pypi/kmodes/json · fetched 2026-08-29 · d509417f8a14
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| nicodv/kmodes | main | 23 |
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Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem