scikit-learn/scikit-learn

Add Kmeans parameter for pruning small clusters

Open

#848 opened on May 10, 2012

View on GitHub
 (6 comments) (0 reactions) (0 assignees)Python (27,020 forks)batch import
ModerateNew Featurehelp wantedmodule:cluster

Repository metrics

Stars
 (66,084 stars)
PR merge metrics
 (Avg merge 10d) (90 merged PRs in 30d)

Description

In Kmeans, often some clusters have only very little data. This might happen for all random initializations. For this case, I would like to have an option to set a minimum cluster size, after which a cluster is dropped and a new one is created.

Contributor guide

Research direction
Explore the KMeans implementation in sklearn/cluster/ kmeans.py. Identify where cluster labels are assigned and where small clusters could be detected after initialization or during iterations. Add a parameter `min cluster size` to drop clusters below that size and reassign samples to a new cluster (e.g., largest cluster or random initialization). Ensure backward compatibility by defaulting to 0.
Tech stack
python
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Pythonscikit learn
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75