Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Concept decompositions for large sparse text data using clustering
Machine Learning
Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognitio
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient agent-based cluster ensembles
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Clustering ensembles and space discretization - A new regard toward diversity and consensus
Pattern Recognition Letters
The Journal of Machine Learning Research
CLICOM: Cliques for combining multiple clusterings
Expert Systems with Applications: An International Journal
Combining multiple clusterings using fast simulated annealing
Pattern Recognition Letters
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
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Clustering is an integral part of pattern recognition problems and is connected to both the data reduction and the data understanding steps. Combining multiple clusterings into an ensemble clustering is critical in many real world applications, particularly for domains with large data sets, high-dimensional feature sets and proprietary data. This paper presents voting active clusters (VACs), a method for combining multiple ''base'' clusterings into a single unified ''ensemble'' clustering that is robust against missing data and does not require all the data to be collected in one central location. In this approach, separate processing centers produce many base clusterings based on some portion of the data. The clusterings of such separate processing centers are then pooled to produce a unified ensemble clustering through a voting mechanism. The major contribution of this work is in providing an adaptive voting method by which the clusterings (e.g., spatially distributed processing centers) update their votes in order to maximize an overall quality measure. Our results show that this method achieves comparable or better performance than traditional cluster ensemble methods in noise-free conditions, and remains effective in noisy scenarios where many traditional methods are inapplicable.