Order-constrained solutions in \(K\)-means clustering: even better than being globally optimal
From MaRDI portal
Publication:998835
DOI10.1007/s11336-008-9058-zzbMath1284.62749OpenAlexW1983340932MaRDI QIDQ998835
Douglas Steinley, Lawrence J. Hubert
Publication date: 30 January 2009
Published in: Psychometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11336-008-9058-z
dynamic programmingconstrained optimizationquadratic assignmentmulticriterion optimization\(K\)-means cluster analysis
Related Items
Disentangling relationships in symptom networks using matrix permutation methods ⋮ A note on the estimation of the Pareto efficient set for multiobjective matrix permutation problems ⋮ A review of multiobjective programming and its application in quantitative psychology ⋮ Applicability and interpretability of Ward's hierarchical agglomerative clustering with or without contiguity constraints ⋮ Music and timbre segmentation by recursive constrained \(K\)-means clustering ⋮ On the behaviour of \(K\)-means clustering of a dissimilarity matrix by means of full multidimensional scaling ⋮ A modified \(k\)-means clustering procedure for obtaining a cardinality-constrained centroid matrix
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- OCLUS: an analytic method for generating clusters with known overlap
- Selection of variables in cluster analysis: An empirical comparison of eight procedures
- Design of hybrids for the minimum sum-of-squares clustering problem
- Simulated annealing for selecting optimal initial seeds in the \(K\)-means algorithm
- Graph coloring, minimum-diameter partitioning, and the analysis of confusion matrices
- Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
- An interactive multiobjective programming approach to combinatorial data analysis
- Compact integer-programming models for extracting subsets of stimuli from confusion matrices
- Combinatorial Data Analysis
- On Grouping for Maximum Homogeneity
- On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions
- Bicriterion Cluster Analysis
- The Structural Representation of Proximity Matrices with MATLAB
- J-MEANS: A new local search heuristic for minimum sum of squares clustering
