A new method for weighted ensemble clustering and coupled ensemble selection

Banerjee, Arko; Pujari, Arun K.; Panigrahi, Chhabi Rani; Pati, Bibudhendu; Nayak, Suvendu Chandan; Weng, Tien‐Hsiung

doi:10.1080/09540091.2020.1866496

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…In this section, the performance of three proposed algorithms (BCESF-SC, BCESF-DC, BCESF-DSC) and seven state-of-the-art CE algorithms are evaluated through experimental settings from various angles. Seven state-of-the-art comparison algorithms include entropy-based consensus clustering (ECC) 62 , weighted hierarchical agglomerative clustering (WHAC) 31 , evidence accumulation clustering (EAC) 45 , probability trajectory-based graph partitioning (PTGP) 63 , dual-granularity weighted ensemble clustering (DGWEC) 52 , ensemble clustering by propagating cluster-wise similarities (ECPCS-HC) 64 and hybrid genetic clustering ensemble algorithm (HGCEA) 33 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The configuration of parameters for the seven comparative CE algorithms is based on the recommendations of the original literature 31 , 33 , 45 , 52 , 62 – 64 . To mitigate the potential impact of randomness on the fairness evaluation, the average index values (NMI and ARI) of each algorithm across 20 runs on each dataset are adopted in our study.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Shi et al 30 designed a multi-objective self-evolution process to discern the relationship between quality and diversity within the source domain dataset, and facilitate the transfer of this established relationship to the target dataset. Banerjee et al 31 introduced a new metric of base clustering quality and diversity, concurrently designing a polynomial heuristic CES algorithm. Khalili et al 32 evaluated the diversity/quality of subsets via the Jaccard similarity measure, and adopted three consensus functions to achieve consistent solutions.…”

Section: Related Workmentioning

confidence: 99%

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Dual-level clustering ensemble algorithm with three consensus strategies

Shan,

Li,

et al. 2023

Sci Rep

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Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years and has achieved numerous noteworthy breakthroughs. Nevertheless, three key issues persist: (1) the majority of CE selection strategies rely on preset parameters or empirical knowledge as a premise, lacking adaptive selectivity; (2) the construction of co-association matrix is excessively one-sided; (3) the CE method lacks a more macro perspective to reconcile the conflicts among different consensus results. To address these aforementioned problems, a dual-level clustering ensemble algorithm with three consensus strategies is proposed. Firstly, a backward clustering ensemble selection framework is devised, and its built-in selection strategy can adaptively eliminate redundant members. Then, at the base clustering consensus level, taking into account the interplay between actual spatial location information and the co-occurrence frequency, two modified relation matrices are reconstructed, resulting in the development of two consensus methods with different modes. Additionally, at the CE consensus level with a broader perspective, an adjustable Dempster–Shafer evidence theory is developed as the third consensus method in present algorithm to dynamically fuse multiple ensemble results. Experimental results demonstrate that compared to seven other state-of-the-art and typical CE algorithms, the proposed algorithm exhibits exceptional consensus ability and robustness.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dual-level clustering ensemble algorithm with three consensus strategies

Shan,

Li,

et al. 2023

Sci Rep

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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.

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