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Automatic detection of outliers and the number of clusters in k-means clustering via Chebyshev-type inequalities

Peter Olukanmi, Fulufhelo V. Nelwamondo, Tshilidzi Marwala, Bhekisipho Twala

2022Neural Computing and Applications22 citationsDOI

Topics & Concepts

CentroidSilhouetteOutlierCluster analysisStandard deviationRange (aeronautics)Anomaly detectionk-means clusteringComputer scienceGeneralizationMathematicsAlgorithmUpper and lower boundsPattern recognition (psychology)Artificial intelligenceStatisticsMaterials scienceMathematical analysisComposite materialAnomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and ModelsAdvanced Statistical Process Monitoring
Automatic detection of outliers and the number of clusters in k-means clustering via Chebyshev-type inequalities | Litcius