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Incremental Bayesian Hierarchical Clustering using Tree Proposals

Juho Lee and Seungjin Choi
Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea

Abstract: In the Bayesian non-parametric models, normalized random measures have significant yield of discrete random measures; where some well-established processes are applied. The posterior inference models use the methods which are easy to be applied and the convergence becomes more usable. The incremental Bayesian hierarchical clustering is created for NRM mixture models. While doing so it is important to consider the efficiency and application of online inferences. We in this work have introduced a hybrid inference algorithm for the mixture models which ensures the benefits of mixture models and Bayesian hierarchical clustering. The proposed trees are ensured for better coverage and use fast convergence to the tree proposals. We have tested the proposal and found the better outcome based both on synthetic and real-world datasets.

Keywords: Bayesian Non-parametric Models, Normalized Random Measures, Hierarchical Clustering Incremental Bayesian Hierarchical Clustering using Tree Proposals

DOI:https://doi.org/10.6025/jcl/2023/14/1/1-10

Full_Text PDF 2.37 MB Download: 76 times

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