Algorithms for clustering data
Algorithms for clustering data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Conceptual clustering in information retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Weighted partition consensus via kernels
Pattern Recognition
Combining multiple clusterings using similarity graph
Pattern Recognition
Soft spectral clustering ensemble applied to image segmentation
Frontiers of Computer Science in China
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
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
Probability-based text clustering algorithm by alternately repeating two operations
Journal of Information Science
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Weighted ensemble of algorithms for complex data clustering
Pattern Recognition Letters
| Hi-index | 0.01 |
Since a large number of clustering algorithms exist, aggregating different clustered partitions into a single consolidated one to obtain better results has become an important problem. In Fred and Jain's evidence accumulation algorithm, they construct a co-association matrix on original partition labels, and then apply minimum spanning tree to this matrix for the combined clustering. In this paper, we will propose a novel clustering aggregation scheme, probability accumulation. In this algorithm, the construction of correlation matrices takes the cluster sizes of original clusterings into consideration. An alternate improved algorithm with additional pre- and post-processing is also proposed. Experimental results on both synthetic and real data-sets show that the proposed algorithms perform better than evidence accumulation, as well as some other methods.