Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
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
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
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clustering with Bregman Divergences
The Journal of Machine Learning Research
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Bioinformatics
Combining multiple clusterings using similarity graph
Pattern Recognition
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
ASOD: Arbitrary shape object detection
Engineering Applications of Artificial Intelligence
A semi-supervised fuzzy clustering algorithm applied to gene expression data
Pattern Recognition
CLICOM: Cliques for combining multiple clusterings
Expert Systems with Applications: An International Journal
Simultaneous clustering and classification over cluster structure representation
Pattern Recognition
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Clustering is the process of grouping objects that are similar, where similarity between objects is usually measured by a distance metric. The groups formed by a clustering method are referred as clusters. Clustering is a widely used activity with multiple applications ranging from biology to economics. Each clustering technique has some advantages and disadvantages. Some clustering algorithms may even require input parameters which strongly affect the result. In most cases, it is not possible to choose the best distance metric, the best clustering method, and the best input argument values for an input data set. Therefore, multiple clusterings can be obtained by several distance metrics, several clustering methods, and several input argument values. And, multiple clusterings can be combined into a new and better quality final clustering. We propose a family of combining multiple clustering algorithms that are memory efficient, scalable, robust, and intuitive. Our new algorithms offer tremendous speed gain and low memory requirements by working at cluster level, while producing very good quality final clusters. Extensive experimental evaluations on some very challenging artificially generated and real data sets from a diverse set of domains establish the usefulness of our methods.