ACM Computing Surveys (CSUR)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering aggregation by probability accumulation
Pattern Recognition
Cluster Analysis
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
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
Weighted partition consensus via kernels
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
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Survey of clustering algorithms
IEEE Transactions on Neural Networks
An indication of unification for different clustering approaches
Pattern Recognition
| Hi-index | 0.11 |
Co-association matrix has been a useful tool in many clustering ensemble techniques as a similarity measure between objects. In this paper, we introduce the weighted-association matrix, which is more expressive than the traditional co-association as a similarity measure, in the sense that it integrates information from the set of partitions in the clustering ensemble as well as from the original data of object representations. The weighted-association matrix is the core of the two main contributions of this paper: a natural extension of the well-known evidence accumulation cluster ensemble method by using the weighted-association matrix and a kernel based clustering ensemble method that uses a new data representation. These methods are compared with simple clustering algorithms as well as with other clustering ensemble algorithms on several datasets. The obtained results ratify the accuracy of the proposed algorithms.