scikit-learn/scikit-learn

Add Kmeans parameter for pruning small clusters

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#848 opened on May 10, 2012

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 (6 comments) (0 reactions) (0 assignees)Python (66,084 stars) (27,020 forks)batch import
ModerateNew Featurehelp wantedmodule:cluster

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

Tech stack
python
Domain
machine learningdata
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.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Pythonscikit learn basicsK means algorithm
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50
Research direction
Review the scikit learn KMeans implementation in `sklearn/cluster/ kmeans.py`. Understand the current initialization and iteration process. Check the issue comments for prior discussion on implementation approaches. Propose adding a `min cluster size` parameter to prune small clusters after each iteration, possibly by merging or reassigning points to the nearest larger cluster.