Generalized Cluster Aggregation
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Summary
A unified framework to solve the clustering aggregation problem, where the aggregated clustering result is obtained by minimizing the (weighted) sum of the Bregman divergence between it and all the input clusterings is proposed.
- Type
- article
- Published
- 2009-07-11
- Cited by
- 37
- References
- 22
- OpenAlex
- https://openalex.org/W52928708
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2600978
Keywords
Cluster analysis, Computer science, Aggregate (composite), Correlation clustering, Constrained clustering
References
- Weighted Consensus Clustering
- Semi-Supervised Clustering via Matrix Factorization
- Constrained Clustering: Advances in Algorithms, Theory, and Applications
- Cluster Ensemble and Its Applications in Gene Expression Analysis
- Solving cluster ensemble problems by bipartite graph partitioning
- Subspace clustering for high dimensional data: a review
- Feature diversity in cluster ensembles for robust document clustering
- Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
- Clustering with Bregman Divergences
- Bayesian cluster ensembles
- Multiclass spectral clustering
- Combining multiple weak clusterings
- A probabilistic framework for semi-supervised clustering
- Fuzzy cluster ensemble and its application on 3D head model classification
- WebACE: a Web agent for document categorization and exploration
- Weighted Clustering Ensembles
- Cluster Ensemble Selection
- Bagging Predictors
- Weighted clustering ensembles
- Bagging Predictors
Cited by
- Spectral Ensemble Clustering
- Efficient Clustering Aggregation Based on Data Fragments
- Automatic malware categorization using cluster ensemble
- Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images
- Class-distribution regularized consensus maximization for alleviating overfitting in model combination
- Ensemble Clustering for Internet Security Applications
- Improving clustering by learning a bi-stochastic data similarity matrix
- Learning a Bi-Stochastic Data Similarity Matrix
- Handbook of Cluster Analysis (provisional top level file)
- Unsupervised Feature-Rich Clustering
- Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization
- Duplicate Detection in Probabilistic Relational Databases
- On the Power of Ensemble: Supervised and Unsupervised Methods Reconciled*
- Cluster ensembles
- Diversity control for improving the analysis of consensus clustering
- Towards Drug Repositioning: A Unified Computational Framework for Integrating Multiple Aspects of Drug Similarity and Disease Similarity
- Clustering for 2D chemical structures
- Which Doctor to Trust: A Recommender System for Identifying the Right Doctors
- Person Re-identification by Multi-hypergraph Fusion
- Applying Assemble Clustering Algorithm and Fault Prediction to Test Case Prioritization
Related papers
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- Solving cluster ensemble problems by bipartite graph partitioning
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- Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
- Nonnegative Matrix Factorization Based Consensus for Clusterings With a Variable Number of Clusters
- Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
- A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters