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Related Experiment Video

Updated: May 14, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Finding reproducible cluster partitions for the k-means algorithm.

Paulo J G Lisboa¹, Terence A Etchells, Ian H Jarman

¹School of Computing and Mathematical Sciences, Byrom Street, Liverpool John Moores University, Liverpool L3 3AF, UK.

BMC Bioinformatics

|February 2, 2013

Summary

This summary is machine-generated.

K-means clustering

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Area of Science:

Data Science
Machine Learning
Bioinformatics

Background:

K-means clustering is a common technique for exploratory data analysis.
The algorithm's sensitivity to initialisation is known, but the lowest sum-of-squares (SSQ) partition is often assumed to be optimal and reproducible.
This assumption holds for small cluster numbers but can be misleading for higher k values.

Purpose of the Study:

To investigate the reliability of the lowest SSQ as an indicator of true cluster structure in k-means.
To develop a method for evaluating the stability of k-means partitions beyond SSQ.
To identify optimal cluster numbers and assess the suitability of the best SSQ solution.

Main Methods:

Extension of stability measures to create a 2-D map of k-means local minima.
Utilisation of stability index to assess partition consistency.
Application to synthetic datasets mimicking breast cancer data and a large bioinformatics dataset.

Main Results:

For higher cluster numbers (k), similar SSQ values can result in significantly different cluster partitions.
The proposed 2-D map visualises local minima, aiding in the identification of stable and meaningful clusters.
Stability index proves crucial for determining if the best SSQ partition is a reliable solution.

Conclusions:

The lowest SSQ in k-means clustering does not always guarantee the most accurate or reproducible cluster partition, especially for larger k.
The developed stability-based mapping method provides a more robust approach to evaluating k-means results.
This approach enhances the reliability of exploratory data analysis and cluster number selection in complex datasets.