Weighted-Object Ensemble Clustering

Ren, Yazhou; Domeniconi, Carlotta; Zhang, Guoji; Yu, Guoxian

doi:10.1109/icdm.2013.80

Cited by 22 publications

(11 citation statements)

References 25 publications

(54 reference statements)

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“…This approach was applied to DNA microarray data analysis and resulted in improved clustering accuracy [22]. Weighted ensemble clustering combines multiple clustering outputs based on their respective quality [23]. Recently, cluster ensembles have been generated by combining outputs from different upstream processing and similarity metrics [24] or different clustering algorithms for cell type identification from scRNA-seq data [25,26].…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Geddes

Kim

Nan

et al. 2019

Preprint

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Background: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.Results: Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used. Conclusions:Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble

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Section: Introductionmentioning

confidence: 99%

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Geddes

Kim

Nan

et al. 2019

Preprint

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“…1. In the generation phase, we implemented the same techniques used by Ren et al [33] in order to generate 10 diverse members. Thus, we used k-means to generate 5 members with a random sampling of 70% of the data, and we calculated the Euclidean distance between the remaining objects and the cluster centres and assigned them to the closest cluster.…”

Section: Methodsmentioning

confidence: 99%

Clustering ensemble method

Alqurashi

Wang

2018

Int. J. Mach. Learn. & Cyber.

105

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A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient.

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“…In Ren et al (2013) and Ren et al (2017), given the ensemble C, the similarity matrix 1 M BB is used to quantify the level of uncertainty in clustering two objects x x x i and x x x j :…”

Section: Weighting Objectsmentioning

confidence: 99%

Weighted Clustering Ensemble: A Review

Zhang

2019

Preprint

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Clustering ensemble has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. One of the arguments for weighted clustering ensemble is that elements (clusterings or clusters) in a clustering ensemble are of different quality, or that objects or features are of varying significance. However, it is not possible to directly apply the weighting mechanisms from classification (supervised) domain to clustering (unsupervised) domain, also because clustering is inherently an ill-posed problem. This paper provides an overview of weighted clustering ensemble by discussing different types of weights, major approaches to determining weight values, and applications of weighted clustering ensemble to complex data. The unifying framework presented in this paper will help clustering practitioners select the most appropriate weighting mechanisms for their own problems.

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Weighted-Object Ensemble Clustering

Cited by 22 publications

References 25 publications

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Clustering ensemble method

Weighted Clustering Ensemble: A Review

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