Clustering Ensemble
5 papers with code • 2 benchmarks • 2 datasets
ionosphere
pathbased
Most implemented papers
The Impact of Random Models on Clustering Similarity
It is often argued that, in order to establish a baseline, clustering similarity should be assessed in the context of a random ensemble of clusterings.
An Internal Validity Index Based on Density-Involved Distance
One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
Ensemble clustering based on evidence extracted from the co-association matrix
The evidence accumulation model is an approach for collecting the information of base partitions in a clustering ensemble method, and can be viewed as a kernel transformation from the original data space to a co-association matrix.
Clustering Ensemble Meets Low-rank Tensor Approximation
The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e. g., spectral clustering.
k-HyperEdge Medoids for Clustering Ensemble
In this paper, the clustering ensemble is formulated as a k-HyperEdge Medoids discovery problem and a clustering ensemble method based on k-HyperEdge Medoids that considers the characteristics of the above two types of clustering ensemble methods is proposed.
