Multilevel k-way hypergraph partitioning
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Multilevel algorithms for multi-constraint graph partitioning
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
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
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
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering aggregation by probability accumulation
Pattern Recognition
Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Exploratory Consensus of Hierarchical Clusterings for Melanoma and Breast Cancer
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On voting-based consensus of cluster ensembles
Pattern Recognition
Weighted partition consensus via kernels
Pattern Recognition
Bioinformatics
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Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some assumptions about the underlying data set. If the assumptions hold, good clusterings can be expected. It is hard, in some cases impossible, to satisfy all the assumptions. Therefore, it is beneficial to apply different clustering methods on the same data set, or the same method with varying input parameters or both. We propose a novel method, DICLENS, which combines a set of clusterings into a final clustering having better overall quality. Our method produces the final clustering automatically and does not take any input parameters, a feature missing in many existing algorithms. Extensive experimental studies on real, artificial, and gene expression data sets demonstrate that DICLENS produces very good quality clusterings in a short amount of time. DICLENS implementation runs on standard personal computers by being scalable, and by consuming very little memory and CPU.