On the behaviour of \(K\)-means clustering of a dissimilarity matrix by means of full multidimensional scaling
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Publication:2066591
DOI10.1007/s11336-021-09757-2zbMath1477.62355OpenAlexW3160565436MaRDI QIDQ2066591
Rodrigo Macías, J. Fernando Vera
Publication date: 14 January 2022
Published in: Psychometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11336-021-09757-2
cluster analysismultidimensional scalingdissimilarity\(K\)-meansadditive constantclustering criteria
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to psychology (62P15)
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Cites Work
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- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features
- Algorithm AS 136: A K-Means Clustering Algorithm
- Intelligent choice of the number of clusters in \(K\)-means clustering: an experimental study with different cluster spreads
- Cluster differences unfolding for two-way two-mode preference rating data
- The analytical solution of the additive constant problem
- Non-stationary spatial covariance structure estimation in oversampled domains by cluster differences scaling with spatial constraints
- A method of predicting the number of clusters using Rand's statistic
- A dual latent class unfolding model for two-way two-mode preference rating data
- Order-constrained solutions in \(K\)-means clustering: even better than being globally optimal
- A latent class multidimensional scaling model for two-way one-mode continuous rating dissimilarity data
- Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables
- Estimating the dimension of a model
- Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima
- Inverse multidimensional scaling
- Optimal variable weighting for ultrametric and additive trees and \(K\)-means partitioning: Methods and software.
- Variance-based cluster selection criteria in a \(K\)-means framework for one-mode dissimilarity data
- A class of instantaneously trained neural networks
- A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation
- Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
- A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning
- Some boundary conditions for a monotone analysis of symmetric matrices
- Modern multidimensional scaling. Theory and applications.
- Approximating a symmetric matrix
- Cluster Analysis
- 10.1162/15324430260185592
- A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering
- Graph Distances in the Data-Stream Model
- Some Statistical Approaches to Multidimensional Scaling Data
- Finding Groups in Data
- Some properties of clasical multi-dimesional scaling
- Finding the Number of Clusters in a Dataset
- Distance stability analysis in multidimensional scaling using the jackknife method
- Least squares quantization in PCM
- A Framework for Feature Selection in Clustering