Machine Learning
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
A clustering method based on boosting
Pattern Recognition Letters
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Moderate diversity for better cluster ensembles
Information Fusion
Weighted Cluster Ensemble Using a Kernel Consensus Function
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations
DS '08 Proceedings of the 11th International Conference on Discovery Science
Clustering aggregation by probability accumulation
Pattern Recognition
Clustering Ensemble Method for Heterogeneous Partitions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Statistical Analysis and Data Mining
A latent variable pairwise classification model of a clustering ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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This paper considers a problem of clustering complex data composed from various structures. A collection of different algorithms is used for the analysis. The main idea is based on the assumption that each algorithm is ''specialized'' (as a rule, gives more accurate partition results) on particular types of structures. The degree of algorithm's ''competence'' is determined by usage of weights attributed to each pair of observations. Optimal weights are specified by the analysis of partial ensemble solutions with use of the proposed model of clustering ensemble. The efficiency of the suggested approach is demonstrated with Monte-Carlo modeling.