Frequent Closed Patterns Based Multiple Consensus Clustering

Al-Najdi, Atheer; Pasquier, Nicolas; Precioso, Fŕed́eric

doi:10.1007/978-3-319-39384-1_2

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…To handle this incompleteness issue, we adapted Al‐Najdi et al. (2016)'s MultiCons clustering approach to handle missing values through MI as proposed recently (Basagaña et al., 2013; Bruckers et al., 2017) but with no constraint on the number of clusters identified in each imputed dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Al‐Najdi, Pasquier, and Precioso (2016) proposed a new algorithm, MultiCons, based on the detection of clustering patterns from similarities between the base partitions, using frequent closed itemsets . Briefly, sets of clusters of the base partitions with common observations are identified and used to build a final partition.…”

Section: Consensus Clusteringmentioning

confidence: 99%

“…To handle different numbers of clusters among the partitions, we propose combining a modified version of the MultiCons consensus clustering algorithm from Al‐Najdi et al. (2016) with an MI method handling both missing and left‐censored data. Briefly, our approach consists of three steps:…”

Section: Consensus Clusteringmentioning

confidence: 99%

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Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

et al. 2020

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Cluster analysis, commonly used to explore large biomedical datasets, can be challenging, notably due to missing data or left‐censored data induced by the sensitivity limits of the biochemical measurement method. Usually, complete‐case analysis, simple imputation, or stochastic simple imputation are applied before clustering. More recently, consensus methods following multiple imputation have been proposed. However, they ignore left‐censoring and do not allow the number of clusters to vary across the partitions of each imputed dataset. Here, we developed a consensus‐based clustering algorithm in which left‐censored data are taken into account using a modified multiple imputation method and the number of clusters is estimated for each imputed dataset. A simulation study was conducted to assess the performance in terms of the number of clusters, the percentage of unclassified observations, and the adjusted Rand index. The simulation results showed that the investigated method works well compared to several alternative approaches. A real‐world application in breast cancer patients showed that the proposed method may reveal novel clusters of patients.

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Section: Discussionmentioning

confidence: 99%

Section: Consensus Clusteringmentioning

confidence: 99%

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Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

et al. 2020

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show abstract

“…These approaches require the input partitions to have the same number of clusters. Al‐Najdi, Pasquier, and Precioso (2016) proposed another consensus algorithm called MultiCons. Briefly, the algorithm is based on detecting clustering patterns from similarities between the input partitions, using frequent closed itemsets .…”

Section: Methodsmentioning

confidence: 99%

Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework

2021

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Semisupervised learning aims to use additional knowledge in the search for data structure. In clinical applications, including predictive information in the construction of a data‐driven classification is of major importance. This work was motivated by a study that aimed to identify different patterns of immune parameters that would be associated with relapse‐free survival in a cohort of breast cancer patients. Supervised and unsupervised objectives can be concomitantly optimized using multiobjective optimization. We propose such a procedure that addresses two challenges in the semisupervised approach, that is, missing data and additional knowledge based on survival time. The former was handled by using multiple imputation and consensus clustering. Survival information was incorporated in the supervised objective through the estimation of a cross‐validation error of a Cox regression. A simulation study was performed to assess the performance of the proposed procedure. On complete datasets, the performances were compared to those of an existing modified multiobjective semisupervised learning method. The added value of including the survival data in the learning process was assessed by comparing the procedure to unsupervised learning. The proposed procedure showed better performance than the existing method, notably in the selection of the number of clusters. On incomplete datasets, the procedure showed little sensitivity to most of its parameters, even though a high number of imputations and partition initialization seeds improved the performance. The performance was degraded with a high proportion of missing data (40%) and with more ambiguous data structures. Simulation results and application on real data support the conclusion that our procedure enables the construction of a classification associated with a right‐censored endpoint on a possibly incomplete dataset.

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“…Like CSPA, this approach in two steps cannot be considered as based on the median partition problem [20]. Lately, [10] proposed consensus based on the MultiCons algorithm [27]. The algorithm presents many advantages, in particular it allows a visualization of the hidden cluster structure in the data set, but it does not aim at minimizing the median partition problem [p. 16] [27].…”

Section: Partitions Pooling After MImentioning

confidence: 99%

Clustering with missing data: which equivalent for Rubin's rules?

Audigier¹,

Niang²

2020

Preprint

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Multiple imputation (MI) is a popular method for dealing with missing values. However, the suitable way for applying clustering after MI remains unclear: how to pool partitions? How to assess the clustering instability when data are incomplete? By answering both questions, this paper proposed a complete view of clustering with missing data using MI. The problem of partitions pooling is here addressed using consensus clustering while, based on the bootstrap theory, we explain how to assess the instability related to observed and missing data. The new rules for pooling partitions and instability assessment are theoretically argued and extensively studied by simulation. Partitions pooling improves accuracy while measuring instability with missing data enlarges the data analysis possibilities: it allows assessment of the dependence of the clustering to the imputation model, as well as a convenient way for choosing the number of clusters when data are incomplete, as illustrated on a real data set.

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Frequent Closed Patterns Based Multiple Consensus Clustering

Cited by 5 publications

References 13 publications

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework

Clustering with missing data: which equivalent for Rubin's rules?

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