Better than the best? Answers via model ensemble in density-based clustering

Casa, Alessandro; Scrucca, Luca; Menardi, Giovanna

doi:10.1007/s11634-020-00423-6

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…However, it is necessary to investigate the potential benefits obtained by adopting more complex probability models. Recently, an adaptation of the proposed algorithm has been used for clustering from an ensemble of Gaussian mixtures [30].…”

Section: Discussionmentioning

confidence: 99%

A fast and efficient Modal EM algorithm for Gaussian mixtures

Scrucca¹

2021

Statistical Analysis

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In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either nonparametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corresponds to a bump of the density. The Modal Expectation‐Maximization (MEM) algorithm is an iterative procedure that can identify the local maxima of any density function. In this contribution, we propose a fast and efficient MEM algorithm to be used when the density function is estimated through a finite mixture of Gaussian distributions with parsimonious component‐covariance structures. After describing the procedure, we apply the proposed MEM algorithm on both simulated and real data examples, showing its high flexibility in several contexts.

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Section: Discussionmentioning

confidence: 99%

A fast and efficient Modal EM algorithm for Gaussian mixtures

Scrucca¹

2021

Statistical Analysis

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“…Nonetheless, model-based techniques (see, e.g., Fraley and Raftery, 2002) might constitute a clever choice, being them coherent with the considered framework. Also ensemble strategies can be adequate, as they aim to combine the strengths of different algorithms and lessen the impact of some otherwise cumbersome choices (see Russell et al, 2015;Wei & McNicholas, 2015;Casa et al, 2021), for some proposals from a model-based standpoint). In addition, powerful initialization strategies for partitioning the data into K groups can as well appropriately serve the purpose (Scrucca & Raftery, 2015).…”

Section: Initializing the Sample Precision Matricesmentioning

confidence: 99%

Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering

2022

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Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these models is jeopardized in high-dimensional spaces, where they tend to be over-parameterized. As a consequence, different solutions have been proposed, often relying on matrix decompositions or variable selection strategies. Recently, a methodological link between Gaussian graphical models and finite mixtures has been established, paving the way for penalized model-based clustering in the presence of large precision matrices. Notwithstanding, current methodologies implicitly assume similar levels of sparsity across the classes, not accounting for different degrees of association between the variables across groups. We overcome this limitation by deriving group-wise penalty factors, which automatically enforce under or over-connectivity in the estimated graphs. The approach is entirely data-driven and does not require additional hyper-parameter specification. Analyses on synthetic and real data showcase the validity of our proposal.

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“…Fraley and Raftery, 2002) might constitute a clever choice, being them coherent with the framework considered here. Also ensemble strategies can be adequate, as they aim to combine the strengths of different algorithms and lessen the impact of some otherwise cumbersome choices (see Russell et al, 2015;Wei and McNicholas, 2015;Casa et al, 2020, for some proposals from a model-based clustering standpoint). Lastly, powerful initialization strategies for partitioning the data into K groups can as well appropriately serve the purpose (Scrucca and Raftery, 2015).…”

Section: Initializing the Sample Precision Matrices ω(0)mentioning

confidence: 99%

Group-wise shrinkage estimation in penalized model-based clustering

Casa¹,

Cappozzo²,

Fop³

2021

Preprint

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Gaussian Graphical Models are widely employed for modelling dependence among variables. Likewise, finite Gaussian mixtures are often the standard way to go for modelbased clustering of continuous features. With the increasing availability of high-dimensional datasets, a methodological link between these two approaches has been established in order to provide a framework for performing penalized model-based clustering in the presence of large precision matrices. Notwithstanding, current methodologies do not account for the fact that groups may possess different degrees of association among the variables, thus implicitly assuming similar levels of sparsity across the classes. We overcome this limitation by deriving group-wise penalty factors, automatically enforcing under or over-connectivity in the estimated graphs. The approach is entirely data-driven and does not require any additional hyper-parameter specification. Simulated data experiments showcase the validity of our proposal.

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Better than the best? Answers via model ensemble in density-based clustering

Cited by 6 publications

References 37 publications

A fast and efficient Modal EM algorithm for Gaussian mixtures

A fast and efficient Modal EM algorithm for Gaussian mixtures

Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering

Group-wise shrinkage estimation in penalized model-based clustering

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