concept

K-means clustering

Also known as: K-means clustering, K-Means

Facts (11)

Sources

Archetypes of open-source business models | Electronic Markets link.springer.com Springer Jun 14, 2022 4 facts

procedureThe researchers applied Ward and K-means algorithms using the Gower distance function with alternating distance metrics (euclidean, manhattan, and binary) to compare clusters and check for robustness.

procedureThe authors used the elbow method (k = 4) based on the k-means algorithm to estimate the optimal number of clusters for their dataset.

procedureThe authors performed Ward's hierarchical cluster algorithm as an alternative approach to k-means, as it generates several possible clusters by gradually merging the two nearest clusters in each step.

procedureThe authors applied both Ward's and K-means algorithms to cluster outcomes to exploit the advantages of both hierarchical and non-hierarchical algorithms, a practice that enhances result robustness as suggested by Ana & Jain (2003).

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer Dec 9, 2025 2 facts

referenceMao, Y.-M., Gan, D., Mwakapesa, D.S., Nanehkaran, Y.A., Tao, T., and Huang, X. (2021) developed a MapReduce-based K-means clustering algorithm, published in The Journal of Supercomputing.

claimMao et al. addressed data skew and initialization sensitivity in K-means clustering via grid-based hashing and adaptive grouping conditions, where symbolic abstraction could support stability and compositionality.

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 2 facts

claimCao et al. (2013) clustered 4000 households from the Irish CER dataset over 18 months using K-means, Self-Organizing Maps (SOM), and hierarchical clustering algorithms, utilizing distance calculations based on the 17 most significant Principal Component Analysis (PCA) components.

accountCao et al. (2013) clustered 4,000 households from the Irish CER dataset over 18 months using K-means, Self-Organizing Maps (SOM), and hierarchical clustering algorithms based on the 17 most significant Principal Component Analysis (PCA) components.

The psychological mechanisms through which digital content ... frontiersin.org Frontiers Nov 12, 2025 2 facts

claimSukmana and Oh (2024) utilized K-means clustering to analyze flight ticket search patterns to enhance digital marketing strategies.

referenceSukmana, H. T. and Oh, L. K. (2024) utilized K-means clustering to analyze flight ticket search patterns to enhance digital marketing in the Journal of Digital Marketing and Digital Currency.

Governance in Practice: How Open Source Projects Define ... - arXiv arxiv.org arXiv 5 days ago 1 fact

procedureThe researchers tested K-Means, Agglomerative Clustering, and DBSCAN algorithms to identify potential groupings of governance roles.