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
An Evolutionary Approach to Clustering Ensemble
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
Combining multiple clusterings using similarity graph
Pattern Recognition
CLICOM: Cliques for combining multiple clusterings
Expert Systems with Applications: An International Journal
DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
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In this paper, we introduce a novel binary method for fast computation of an objective function to measure inter and intra class similarities, which is used for combining multiple clusterings. Our method has the advantages of using less memory and CPU time. Moreover, compared with the conventional technique, we reduce the time complexity of the problem considerably. Experimental test results demonstrate the effectiveness of our new method.