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Hierarchical clustering with discrete latent variable models and the integrated classification likelihood

Author

Listed:
  • Etienne Côme

    (COSYS/GRETTIA, Université Gustave-Eiffel)

  • Nicolas Jouvin

    (Université Paris 1 Panthéon-Sorbonne
    FP2M, CNRS FR 2036)

  • Pierre Latouche

    (FP2M, CNRS FR 2036
    Université de Paris, MAP5, CNRS)

  • Charles Bouveyron

    (Université Côte d’Azur, CNRS, Laboratoire J.A. Dieudonné
    Inria, Maasai Research Team)

Abstract

Finding a set of nested partitions of a dataset is useful to uncover relevant structure at different scales, and is often dealt with a data-dependent methodology. In this paper, we introduce a general two-step methodology for model-based hierarchical clustering. Considering the integrated classification likelihood criterion as an objective function, this work applies to every discrete latent variable models (DLVMs) where this quantity is tractable. The first step of the methodology involves maximizing the criterion with respect to the partition. Addressing the known problem of sub-optimal local maxima found by greedy hill climbing heuristics, we introduce a new hybrid algorithm based on a genetic algorithm which allows to efficiently explore the space of solutions. The resulting algorithm carefully combines and merges different solutions, and allows the joint inference of the number K of clusters as well as the clusters themselves. Starting from this natural partition, the second step of the methodology is based on a bottom-up greedy procedure to extract a hierarchy of clusters. In a Bayesian context, this is achieved by considering the Dirichlet cluster proportion prior parameter $$\alpha $$ α as a regularization term controlling the granularity of the clustering. A new approximation of the criterion is derived as a log-linear function of $$\alpha $$ α , enabling a simple functional form of the merge decision criterion. This second step allows the exploration of the clustering at coarser scales. The proposed approach is compared with existing strategies on simulated as well as real settings, and its results are shown to be particularly relevant. A reference implementation of this work is available in the R-package greed accompanying the paper.

Suggested Citation

  • Etienne Côme & Nicolas Jouvin & Pierre Latouche & Charles Bouveyron, 2021. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 957-986, December.
  • Handle: RePEc:spr:advdac:v:15:y:2021:i:4:d:10.1007_s11634-021-00440-z
    DOI: 10.1007/s11634-021-00440-z
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    References listed on IDEAS

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    1. Wyse, Jason & Friel, Nial & Latouche, Pierre, 2017. "Inferring structure in bipartite networks using the latent blockmodel and exact ICL," Network Science, Cambridge University Press, vol. 5(1), pages 45-69, March.
    2. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
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    4. Marco Bertoletti & Nial Friel & Riccardo Rastelli, 2015. "Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 177-199, August.
    5. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
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    Cited by:

    1. Marino, Maria Francesca & Pandolfi, Silvia, 2022. "Hybrid maximum likelihood inference for stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).

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