Bayesian hierarchical clustering

By Katherine A. Heller, Zoubin Ghahramani. International Conference on Machine Learning, 2005

419 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bcf060787b10fe684600633c7aadff3f00e02f18

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Citations in the corpus

Tree-Structured Stick Breaking for Hierarchical Data (2010, methodology): “Unsupervised learning of densities and nested mixtures has received particular attention via finite-depth trees [1], diffusive branching processes [2] and hierarchical clustering [3, 4].”
Fast hierarchical Bayesian analysis of population structure (2018, methodology): “As described in Heller and Ghahramani (26), for any given set of hyperparameters, the root node of the hierarchy approximates the probability of the data given that setting of the hyperparameters.”
The Indian Buffet Process: An Introduction and Review (2011, methodology): “Recent work has extended Dirichlet process mixture models in a number of directions, making it possible to use nonparametric Bayesian methods to discover the kinds of structure common in machine learning: hierarchies (Blei et al., 2004; Heller and Ghahramani, 2005; Neal, 2003; Teh et al., 2008), top”
Greedy Learning of Binary Latent Trees (2011, background): “There is also some recent work on probabilistic hierarchical clustering, e.g. Heller and Ghahramani (2005); Friedman (2003).”
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (2021, unknown): “AHC uses euclidean distance between closest data points or clusters mean to compute the similarity or dissimilarity of neighbouring clusters [38].”
Kernel Embeddings of Latent Tree Graphical Models (2011, unknown): “Experiments on simulated and real data show the advantage of our proposed approach.”
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017, background): “There is a large number of clustering algorithms in literature, which can be grouped into different perspectives, such as hierarchical [9, 51], centroid-based [20, 3, 27], graphbased [38, 28, 48, 25], sequential (temporal) [11, 37, 66], regression model based [7, 47], and subspace clustering models ”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, background): “Heller and Ghahramani (2005) have proposed a Bayesian agglomerative hierarchical clustering algorithm, that evaluates marginal likelihoods of a probabilistic model of the data, and uses a model-based criterion to determine which clusters to be merged.”