Paralinear distance and its algorithm for hierarchical clustering of high-dimensional discrete variables
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Publication:6548456
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)
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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