Selective Clustering Ensemble Based on Covariance

Lu, Xuyao; Yang, Yan; Wang, Hongjun

doi:10.1007/978-3-642-38067-9_16

Cited by 10 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more e cient consensus solution can be obtained if the ensemble members are different from each other (diversity) and have satisfactory quality. [40,62] Especially when the ensemble size is small, combining identical clustering solutions leads to an inaccurate consensus solution. [32] In the supervised classi cation task, the classi ers are ranked based on their individual performance on a held-out test set, and the best ones are picked.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Ensemble Learning Using High-dimensional Spectroscopy Data of Organic Compounds

Massena

2022

Preprint

View full text Add to dashboard Cite

Cluster ensemble (CE) is an ensemble learning strategy for unsupervised learning (clustering) that uses a set of clustering solutions to achieve more comprehensive clustering results than traditional single clustering approaches. This meta-learning formalism helps users overcome the dilemma of choosing the appropriate clustering algorithm and parameters for a given data set. Unfortunately, not all clustering solutions in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary “views” of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.

show abstract

Section: Related Workmentioning

confidence: 99%

Unsupervised Ensemble Learning Using High-dimensional Spectroscopy Data of Organic Compounds

Massena

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The main problem in the clustering ensemble selection is how to evaluate each cluster. The literature consists of different quality and diversity measures implemented to ensemble members [21,22]. The basis of these measures is the match index between the two partitions.…”

Section: Related Workmentioning

confidence: 99%

“…In another study, Hadjitodorov et al [14] used the ARI diversity measure to select ensemble members. Lu et al [21] suggested a diversity criterion based on covariance. Alizadeh et al [26] proposed a CES method in which clusters were selected based on diversity and quality measures.…”

Section: Related Workmentioning

confidence: 99%

Clustering ensemble selection based on the extended Jaccard measure

Khalili¹,

Rabbani²,

Akbari³

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Clustering ensemble selection has shown high efficiency in the improvement of the quality of clustering solutions. This technique comprises two important metrics: diversity and quality. It has been empirically proved that ensembles of higher effectiveness can be achieved through taking into consideration the diversity and quality simultaneously. However, the relationships between these two metrics in base clusterings have remained uncertain. This paper suggests a new hierarchical selection algorithm using a diversity/quality measure based on the jaccard similarity measure. In the proposed algorithm, the selection of the subsets of the clustering partitions is done based on their diversity measures. The proposed diversity measure (in two types of pair-wise diversity and hybrid diversity) is applied to the proposed algorithm. Hypergraph-Partitioning Algorithm (HGPA), Cluster-based Similarity Partition Algorithm (CSPA), and Meta-CLustering Algorithm (MCLA) were used to obtain the consensus solution and cluster ensemble selection results with a hierarchical method. The experimental results on 14 datasets showed that selecting a subset of base clusterings using the proposed algorithm led to more accurate results compared to those of the full ensemble. The effectiveness and robustness of the proposed algorithm were demonstrated in comparison with the full ensemble. The comparative results showed that the proposed method by new diversity measure outperformed the full ensemble.

show abstract