Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
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Publication:928853
DOI10.1016/j.jmva.2007.07.002zbMath1141.62052OpenAlexW2171523622WikidataQ60471616 ScholiaQ60471616MaRDI QIDQ928853
Publication date: 11 June 2008
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2007.07.002
breakdown pointmixture modelhierarchical cluster analysistrimmed \(k\)-meansmodel-based cluster analysisaverage silhouette width
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)
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Uses Software
Cites Work
- Metric and Euclidean properties of dissimilarity coefficients
- An entropy criterion for assessing the number of clusters in a mixture model
- Estimating the dimension of a model
- Space-contracting, space-dilating, and positive admissible clustering algorithms
- Trimmed \(k\)-means: An attempt to robustify quantizers
- The choice of vantage objects for image retrieval.
- Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
- Clustering and classification based on the L\(_{1}\) data depth
- Breakdown points for maximum likelihood estimators of location-scale mixtures
- Breakdown and groups. (With discussions and rejoinder)
- Finding Groups in Data
- The Influence Curve and Its Role in Robust Estimation
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Model-Based Gaussian and Non-Gaussian Clustering
- Robustness Properties of k Means and Trimmed k Means
- Robustness of ML Estimators of Location-Scale Mixtures
- Automatische Klassifikation
- Admissible clustering procedures
- A General Qualitative Definition of Robustness
- Some quantitative relationships between two types of finite sample breakdown point
- A new look at the statistical model identification
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