ArticleBMC bioinformatics2013
Finding reproducible cluster partitions for the k-means algorithm.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.The journal of allergy and clinical immunology. Global · 2026Article
- Polymer-metal oxide interfaces in XHNBR/PA6 blends: computational insights toward sustainable crosslinking.RSC advances · 2026Article
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- Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering.Briefings in bioinformatics · 2022Article
- Audiometric Phenotypes of Noise-Induced Hearing Loss by Data-Driven Cluster Analysis and Their Relevant Characteristics.Frontiers in medicine · 2021Article
- Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach.BMJ open diabetes research & care · 2020 · on this mapArticle
- SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines.BMC bioinformatics · 2018Article
- Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine.Genes · 2018Review
- Pattern Recognition Analysis Reveals Unique Contrast Sensitivity Isocontours Using Static Perimetry Thresholds Across the Visual Field.Investigative ophthalmology & visual science · 2017Article
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4 authors.
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Abstract
K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.
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