Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Reinterpreting the Category Utility Function
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On Clustering Validation Techniques
Journal of Intelligent Information Systems
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
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
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Clustering aggregation by probability accumulation
Pattern Recognition
A novel framework for discovering robust cluster results
DS'06 Proceedings of the 9th international conference on Discovery Science
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Combining multiple clusterings using similarity graph
Pattern Recognition
Partition selection approach for hierarchical clustering based on clustering ensemble
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Image segmentation fusion using general ensemble clustering methods
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Estimation of the number of clusters using heterogeneous multiple classifier system
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Weighted association based methods for the combination of heterogeneous partitions
Pattern Recognition Letters
DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An indication of unification for different clustering approaches
Pattern Recognition
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Ensemble clustering by means of clustering embedding in vector spaces
Pattern Recognition
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The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods.