A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Integration of Cluster Ensemble and Text Summarization for Gene Expression Analysis
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Categorization and Keyword Identification of Unlabeled Documents
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Moderate diversity for better cluster ensembles
Information Fusion
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised feature weighting with multi niche crowding genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Learning multiple nonredundant clusterings
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering complex data with group-dependent feature selection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A new clustering algorithm with the convergence proof
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Optimal clustering in the context of overlapping cluster analysis
Information Sciences: an International Journal
Projective clustering ensembles
Data Mining and Knowledge Discovery
Adaptive evidence accumulation clustering using the confidence of the objects' assignments
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Multiobjective projection pursuit for semisupervised feature extraction
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
Journal of Information Science
A clustering ensemble framework based on elite selection of weighted clusters
Advances in Data Analysis and Classification
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Pairwise similarity for cluster ensemble problem: link-based and approximate approaches
Transactions on Large-Scale Data- and Knowledge-centered systems IX
Combining supervised and unsupervised models via unconstrained probabilistic embedding
Information Sciences: an International Journal
Weighted ensemble of algorithms for complex data clustering
Pattern Recognition Letters
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Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this article, we address the problem of combining multiple weighted clusters that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.