ArticlePloS one2024
The impact of neglecting feature scaling in k-means clustering.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Patterns, drivers, and trends of urban cooling demand across global cities.Nature communications · 2026Article
- Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.The journal of allergy and clinical immunology. Global · 2026Article
- AlphaFold-Multimer modelling of linked nAChR subunits challenges concatemer design assumptions.Scientific reports · 2026Article
- Portable Electronic Olfactometer for Non-Invasive Screening of Canine Ehrlichiosis: A Proof-of-Concept Study Using Machine Learning.Veterinary sciences · 2026Article
- Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.Scientific reports · 2025Article
- Location optimization of cold chain logistics parks based on Bayesian probability theory and K-means clustering analysis in China.Scientific reports · 2025Article
- Differences in consonant confusion patterns between bimodal cochlear implant users with greater and less bimodal advantage.Frontiers in psychology · 2025Article
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1 author.
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Abstract
Despite the popularity of k-means clustering, feature scaling before applying it can be an essential yet often neglected step. In this study, feature scaling via five methods: Z-score, Min-Max normalization, Percentile transformation, Maximum absolute scaling, or RobustScaler beforehand was compared with using the raw (i.e., non-scaled) data to analyze datasets having features with different or the same units via k-means clustering. The results of an experimental study show that, for features with different units, scaling them before k-means clustering provided better accuracy, precision, recall, and F-score values than when using the raw data. Meanwhile, when features in the dataset had the same unit, scaling them beforehand provided similar results to using the raw data. Thus, scaling the features beforehand is a very important step for datasets with different units, which improves the clustering results and accuracy. Of the five feature-scaling methods used in the dataset with different units, Z-score standardization and Percentile transformation provided similar performances that were superior to the other or using the raw data. While Maximum absolute scaling, slightly more performances than the other scaling methods and raw data when the dataset contains features with the same unit, the improvement was not significant.
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