# Consensus Big Data Clustering for Bayesian Mixture Models
## 2 Related Work
Real-world applications of these clustering techniques can be found in various domains, such as gene expression analysis, cell classification in flow cytometry experiments, and protein localization estimation [20; 21; 22]. Recent advancements in ensemble clustering have addressed various challenges posed by high-dimensional data and complex structures. [PERSON] and [PERSON] [23] proposed a consensus clustering approach specifically designed for high-dimensional data, while [PERSON] et al. [24] developed a multi-view ensemble clustering approach using a joint affinity matrix to improve the quality of clustering. [PERSON] et al. [25] introduced an ensemble hierarchical clustering algorithm that considers merits at both cluster and partition levels. In addition, [PERSON] et al. [26] presented a clustering ensemble method based on structured hypergraph learning, and [PERSON] and [PERSON] [27] proposed an ensemble and multi-view clustering method based on Kolmogorov complexity. [PERSON] et al. [28] tackled the challenge of high-dimensional data by developing a multidiversified ensemble clustering approach, focusing on various aspects such as subspaces, metrics, and more. [PERSON] et al. [29] also proposed an ultra-scalable spectral clustering and ensemble clustering technique. [PERSON] et al. [30] developed a Markov clustering ensemble method, and [PERSON] et al. [31] presented a fast multi-view clustering approach via ensembles for scalability, superiority, and simplicity. These studies showcase the diverse range of ensemble clustering techniques developed to address complex data challenges and improve the performance of clustering algorithms. Clustering ensemble techniques have been developed and applied across various domains, addressing the challenges and limitations of traditional clustering methods. [PERSON] et al. [32] concentrated on the analysis of scRNA-seq data, discussing the methods, applications, and difficulties associated with ensemble clustering in this field. [PERSON] and [PERSON] [33] presented an exhaustive review of cluster ensembles, highlighting recent extensions and applications. [PERSON] [34] examined the ensemble of specialised cadherin clusters in adherens junctions, demonstrating the versatility of ensemble clustering methods. [PERSON] and [PERSON] [35] introduced Ensemble Clustering based on Bayesian Net work (ECBN) inference for single-cell RNA-seq data analysis, offering a novel method for addressing the difficulties inherent to this data format. [PERSON] et al. [36] proposed an ultra-scalable ensemble clustering method for cell-type recognition using scRNA-seq data of Alzheimer's disease. [PERSON] et al. [37] developed an ensemble consensus clustering method, scEFSC, for accurate single-cell RNA-seq data analysis based on multiple feature selections. [PERSON] and [PERSON] [38] introduced a semi-supervised consensus clustering method for gene expression data analysis, while [PERSON] et al. [39] explored knowledge-based cluster ensemble approaches for cancer discovery from biomolecular data. Finally, [PERSON] et al. [40] proposed a consensus clustering approach using a constrained self-organizing map and an improved Cop-Kmeans ensemble for intelligent decision support systems, showcasing the broad applicability of ensemble clustering techniques in various fields. Bayesian mixture models, with their adaptable densities, are highly attractive for data analysis across various types. The number of clusters \(K\) can be inferred directly from the data as a random variable, resulting in joint modelling of \(K\) and the clustering process [41; 42; 43; 44; 45; 46]. Inference of the number of clusters can be achieved through methods such as the Dirichlet process [41], finite mixture models [42; 43], or over-fitting mixture models [44]. These models have found success in a wide range of biological applications, including gene expression profiles [20], cell classification in flow cytometry experiments [21; 47] and scRNAseq experiments [48], as well as protein localization estimation [22].