Analysis of multilevel graph partitioning
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-Based cumulative ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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In this paper, the accuracies of four meta-clustering algorithms and five different base-clustering algorithms are compared. These algorithms come from different knowledge areas such as statistics, neural networks and machine learning. The main advantages of these algorithms are their adaptiveness to some specific datasets. The ensembles are based on bagging, voting and graph partitioning. These ensembles use relabeling techniques to find their accuracy. Mutual information and misclassification error rate are used to compare the performance of these algorithms. One artificial and three real-world datasets available at the UCI Machine Learning Repository are used in the experiments.