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Parameter modification for clustering criteria

https://doi.org/10.1007/bf01896986
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Pattern Recognition with Fuzzy Objective Function Algorithms
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Linear and Nonlinear Programming
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Clustering Analysis
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Automatische Klassifikation
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Local convergence analysis of a grouped variable version of coordinate descent
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Numerical classification of proximity data with assignment measures
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Classification as a tool of research
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Cluster dissection and analysis
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Linear and nonlinear programming.
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9 The classification and mixture maximum likelihood approaches to cluster analysis
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On Some Invariant Criteria for Grouping Data
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Asymptotic Behaviour of Classification Maximum Likelihood Estimates
inferredcites →https://doi.org/10.1093/biomet/69.2.417openalex-snapshot
Optimization Methods of Cluster Analysis
inferredcites →https://doi.org/10.1109/tpami.1984.4767478openalex-snapshot
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
inferredcites →https://doi.org/10.1137/1026034openalex-snapshot
Mixture Densities, Maximum Likelihood and the EM Algorithm
inferredcites →https://doi.org/10.1146/annurev.ps.31.020180.003135openalex-snapshot
Multidimensional Scaling
inferredcites →https://doi.org/10.1207/s15327906mbr0503_6openalex-snapshot
PATTERN CLUSTERING BY MULTIVARIATE MIXTURE ANALYSIS
inferredcites →https://doi.org/10.2307/2529003openalex-snapshot
Clustering Methods Based on Likelihood Ratio Criteria
inferredcites →https://doi.org/10.2307/2529563openalex-snapshot
389: Separating Mixtures of Normal Distributions
inferredcites →https://doi.org/10.2307/2530520openalex-snapshot
Clustering Criteria and Multivariate Normal Mixtures
inferredcites →https://doi.org/10.2307/2530539openalex-snapshot
Klassifikation und Erkenntnis III: Numerische Klassifikation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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