Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
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 Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Ensemble clustering with voting active clusters
Pattern Recognition Letters
An Evolutionary Approach to Clustering Ensemble
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
A scalable framework for cluster ensembles
Pattern Recognition
Expert Systems with Applications: An International Journal
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Spectral clustering ensemble for image segmentation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
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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. Then, the clusterings obtained can be combined into a final clustering having better overall quality. Combining multiple clusterings into a final clustering which has better overall quality has gained significant importance recently. Our contributions are a novel method for combining a collection of clusterings into a final clustering which is based on cliques, and a novel output-sensitive clique finding algorithm which works on large and dense graphs and produces output in a short amount of time. Extensive experimental studies on real and artificial data sets demonstrate the effectiveness of our contributions.