Enhancing the selection of a model-based clustering with external categorical variables

Baudry, Jean-Patrick; Cardoso, Margarida G. M. S.; Celeux, Gilles; Amorim, Maria José; Ferreira, Ana Sousa

doi:10.1007/s11634-014-0177-3

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…To deal with the problem of non-substantive classes, one might select the number of classes based on their relationship with external, substantively meaningful, variables (Baudry et al 2014). While this approach does prevent the modeling of non-substantive classes, the nuisance local dependence within the substantive classes remains.…”

Section: Introductionmentioning

confidence: 99%

Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

Oberski

2015

Adv Data Anal Classif

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Latent class analysis (LCA) for categorical data is a model-based clustering and classification technique applied in a wide range of fields including the social sciences, machine learning, psychiatry, public health, and epidemiology. Its central assumption is conditional independence of the indicators given the latent class, i.e. "local independence"; violations can appear as model misfit, often leading LCA practitioners to increase the number of classes. However, when not all of the local dependence is of substantive scientific interest this leads to two options, that are both problematic: modeling uninterpretable classes, or retaining a lower number of substantive classes but incurring bias in the final results and classifications of interest due to remaining assumption violations. This paper suggests an alternative procedure, applicable in cases when the number of substantive classes is known in advance, or when substantive interest is otherwise well-defined. I suggest, in such cases, to model substantive local dependencies as additional discrete latent variables, while absorbing nuisance dependencies in additional parameters. An example application to the estimation of misclassification and turnover rates of the decision to vote in elections of 9510 Dutch residents demonstrates the advantages of this procedure relative to increasing the number of classes.

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Section: Introductionmentioning

confidence: 99%

Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

Oberski

2015

Adv Data Anal Classif

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“…a gene is either annotated or unannotated), it may seem natural to directly use the SICL defined in Equation (5). However, in contrast to the situation considered by Baudry et al (2014), gene annotation information is often incomplete. More precisely, for each of the G annotation terms, indexed by g, the available information u g is as follows:…”

Section: Taking Genome Annotations Into Accountmentioning

confidence: 96%

“…More recently, Baudry et al (2014) proposed an ICL-like criterion that takes advantage of the potential explicative ability of external categorical variables u = (u 1 , …, u R ) where = 1 r i u indicates that the gene i is in category ℓ for the r th external categorical variable and 0 otherwise. The idea is to choose a classification z based on y that is coherent with u.…”

Section: Model-based Clustering and Model Selectionmentioning

confidence: 99%

A model selection criterion for model-based clustering of annotated gene expression data

Gallopin¹,

Celeux²,

Jaffrézic³

et al. 2015

Statistical Applications in Genetics and Molecular Biology

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Abstract:In co-expression analyses of gene expression data, it is often of interest to interpret clusters of co-expressed genes with respect to a set of external information, such as a potentially incomplete list of functional properties for which a subset of genes may be annotated. Based on the framework of finite mixture models, we propose a model selection criterion that takes into account such external gene annotations, providing an efficient tool for selecting a relevant number of clusters and clustering model. This criterion, called the integrated completed annotated likelihood (ICAL), is defined by adding an entropy term to a penalized likelihood to measure the concordance between a clustering partition and the external annotation information. The ICAL leads to the choice of a model that is more easily interpretable with respect to the known functional gene annotations. We illustrate the interest of this model selection criterion in conjunction with Gaussian mixture models on simulated gene expression data and on real RNA-seq data.

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“…Although a variety of finite mixture models has been extensively studied and developed in the literature (Baudry et al 2015;Subedi and McNicholas 2014;Lee and McLachlan 2013;Morris et al 2013), the Gaussian case has received a special attention (Morlini 2012;Nguyen and McLachlan 2015;Hennig 2010;Scrucca and Raftery 2015).…”

Section: Mixture Of Gaussian Modelsmentioning

confidence: 99%

“…However, since the number of components for each chunklet is not specified a priori, it is preferable to use an algorithm which detects the number of clusters automatically. Therefore, we combine our method with cross-entropy clustering (CEC) (Tabor and Spurek 2014;Spurek et al 2013;Tabor and Misztal 2013;Śmieja andTabor 2013, 2015b) which can be seen as a model-based clustering (McLachlan and Krishnan 2008;Morlini 2012;Subedi and McNicholas 2014;Baudry et al 2015) and determines the final number of groups.…”

Section: Introductionmentioning

confidence: 99%

Constrained clustering with a complex cluster structure

Śmieja

Wiercioch

2016

Adv Data Anal Classif

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In this contribution we present a novel constrained clustering method, Constrained clustering with a complex cluster structure (C4s), which incorporates equivalence constraints, both positive and negative, as the background information. C4s is capable of discovering groups of arbitrary structure, e.g. with multi-modal distribution, since at the initial stage the equivalence classes of elements generated by the positive constraints are split into smaller parts. This provides a detailed description of elements, which are in positive equivalence relation. In order to enable an automatic detection of the number of groups, the cross-entropy clustering is applied for each partitioning process. Experiments show that the proposed method achieves significantly better results than previous constrained clustering approaches. The advantage of our algorithm increases when we are focusing on finding partitions with complex structure of clusters.

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Enhancing the selection of a model-based clustering with external categorical variables

Cited by 14 publications

References 14 publications

Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

A model selection criterion for model-based clustering of annotated gene expression data

Constrained clustering with a complex cluster structure

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