Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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Clustering-ensemble can protect private information, process distributed data and reuse of knowledge, besides, noise and outliers have little effect on clustering results. This paper proposes a new clustering ensemble algorithm, based on voting, introduce correlation to represent the similarity of clusters. The correspondence between labeled vectors can be established because clusters with lager correlation share the same cluster labels. After reunification, the labeled vectors will be used to decide the final cluster result. Analysis and experiments show that the proposed algorithm could be used to clustering-ensemble and effectively improve the clustering results.