A multi-view ensemble clustering approach using joint affinity matrix

Niu, Xueying; Zhang, Chaowei; Zhao, Xiao‐Fan; Hu, Lihua; Zhang, Jifu

doi:10.1016/j.eswa.2022.119484

Cited by 9 publications

(1 citation statement)

References 26 publications

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“…Recent advancements in ensemble clustering have addressed various challenges posed by high-dimensional data and complex structures. Yan and Liu [23] proposed a consensus clustering approach specifically designed for high-dimensional data, while Niu et al [24] developed a multi-view ensemble clustering approach using a joint affinity matrix to improve the quality of clustering. Huang et al [25] introduced an ensemble hierarchical clustering algorithm that considers merits at both cluster and partition levels.…”

Section: Related Workmentioning

confidence: 99%

Consensus Big Data Clustering for Bayesian Mixture Models

Karras

Giotopoulos

et al. 2023

Algorithms

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In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consensus clustering ensemble that yields more significant outcomes compared to individual models. Another valuable technique for cluster analysis is Bayesian mixture modelling, which is known for its adaptability in determining cluster numbers. Traditional inference methods such as Markov chain Monte Carlo may be computationally demanding and limit the exploration of the posterior distribution. In this work, we introduce an innovative approach that combines consensus clustering and Bayesian mixture models to improve big-data management and simplify the process of identifying the optimal number of clusters in diverse real-world scenarios. By addressing the aforementioned hurdles and boosting accuracy and efficiency, our method considerably enhances cluster analysis. This fusion of techniques offers a powerful tool for managing and examining large and intricate datasets, with possible applications across various industries.

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Section: Related Workmentioning

confidence: 99%

Consensus Big Data Clustering for Bayesian Mixture Models

Karras

Giotopoulos

et al. 2023

Algorithms

View full text Add to dashboard Cite

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PCS-granularity weighted ensemble clustering via Co-association matrix

Wu,

Cai,

et al. 2024

Appl Intell

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A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

Li,

Xu,

et al. 2024

Neural Process Lett

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Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.

show abstract

A multi-view ensemble clustering approach using joint affinity matrix

Cited by 9 publications

References 26 publications

Consensus Big Data Clustering for Bayesian Mixture Models

Consensus Big Data Clustering for Bayesian Mixture Models

PCS-granularity weighted ensemble clustering via Co-association matrix

A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

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