A new measure for assessment of clustering based on kernel density estimation

DOI10.1080/03610926.2022.2032168arXiv2201.02030OpenAlexW4210702109MaRDI QIDQ6164679

Soumita Modak

Publication date: 28 July 2023

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.02030

zbMATH Keywords

kernel density estimationinterpoint distancenonparametric statisticclustering accuracyhigh-dimensional applicabilitylight curve of variable starAlon data

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion?
• CID: an efficient complexity-invariant distance for time series
• An introduction to copulas.
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• A new nonparametric interpoint distance-based measure for assessment of clustering
• Well-Separated Clusters and Optimal Fuzzy Partitions
• Finite mixture models
• Unsupervised classification of eclipsing binary light curves throughk-medoids clustering
• Clustering of gamma-ray bursts through kernel principal component analysis
• A new algorithm for clustering based on kernel density estimation

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