Paralinear distance and its algorithm for hierarchical clustering of high-dimensional discrete variables

DOI10.1016/j.ijar.2024.109133MaRDI QIDQ6548456

Xiao-Fei Wang, Jian-hua Guo, Shuai Wang, Li-zhu Hao

Publication date: 1 June 2024

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

zbMATH Keywords

variable clusteringlatent tree modelparalinear distance

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Model-based multidimensional clustering of categorical data
• Latent tree models for hierarchical topic detection
• Spectral methods for learning discrete latent tree models
• Greedy learning of latent tree models for multidimensional clustering
• Model-based clustering of high-dimensional data: variable selection versus facet determination
• Decomposition of structural learning about directed acyclic graphs
• Finding the minimal set for collapsible graphical models
• Collapsibility and response variables in contingency tables
• A few logs suffice to build (almost) all trees (I)
• Performance study of phylogenetic methods: (unweighted) quartet methods and neighbor-joining
• 10.1162/jmlr.2003.3.4-5.993
• A Survey on Latent Tree Models and Applications
• Association Pattern Discovery Via Theme Dictionary Models
• Learning Latent Tree Graphical Models
• Approximating discrete probability distributions with dependence trees
• The classical groups, their invariants and representations.
• The Elements of Statistical Learning

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