Tensorized bipartite graph learning for multi-view clustering
Despite the impressive clustering performance and efficiency in characterizing both the
relationship between the data and cluster structure, most existing graph-based multi-view …
relationship between the data and cluster structure, most existing graph-based multi-view …
Ensemble clustering via fusing global and local structure information
J Xu, T Li, D Zhang, J Wu - Expert Systems with Applications, 2024 - Elsevier
Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base
clusterings. Most existing methods rely on a co-association (CA) matrix that describes the …
clusterings. Most existing methods rely on a co-association (CA) matrix that describes the …
Clustering ensemble via structured hypergraph learning
Clustering ensemble integrates multiple base clustering results to obtain a consensus result
and thus improves the stability and robustness of the single clustering method. Since it is …
and thus improves the stability and robustness of the single clustering method. Since it is …
Divclust: Controlling diversity in deep clustering
Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …
Deep Learning has recently been applied with significant success. However, an aspect of …
Adaptive consensus clustering for multiple k-means via base results refining
Consensus clustering, which learns a consensus clustering result from multiple weak base
results, has been widely studied. However, conventional consensus clustering methods only …
results, has been widely studied. However, conventional consensus clustering methods only …
Active clustering ensemble with self-paced learning
A clustering ensemble provides an elegant framework to learn a consensus result from
multiple prespecified clustering partitions. Though conventional clustering ensemble …
multiple prespecified clustering partitions. Though conventional clustering ensemble …
Self-paced adaptive bipartite graph learning for consensus clustering
Consensus clustering provides an elegant framework to aggregate multiple weak clustering
results to learn a consensus one that is more robust and stable than a single result …
results to learn a consensus one that is more robust and stable than a single result …
Bi-level ensemble method for unsupervised feature selection
Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …
increasingly more attention. However, due to the absence of labels, unsupervised feature …
Data-centric graph learning: A survey
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
Ensemble clustering via co-association matrix self-enhancement
Ensemble clustering integrates a set of base clustering results to generate a stronger one.
Existing methods usually rely on a co-association (CA) matrix that measures how many …
Existing methods usually rely on a co-association (CA) matrix that measures how many …