Cluster Ensembles A Knowledge Reuse Framework for Combining Partitionings

By Alexander Strehl, Joydeep Ghosh. AAAI/IAAI, 2002

441 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c15aa7143701a63b3ffa8a8a4ab768200a9009da

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Citations in the corpus

A Unified Framework for Model-based Clustering (2003, methodology): “Y , governing the cluster labels, and a r.v. ˆ Y , governing the class labels, is a superior measure to purity or entropy (Dom, 2001; Strehl and Ghosh, 2002).”
An analysis of social network-based Sybil defenses (2010, methodology): “To compare the rankings, we use mutual information [28], which measures the similarity of two partitionings of a set.”
A Survey of Evolutionary Algorithms for Clustering (2009, methodology): “In spite of the difficulty associated with this issue, there are several works investigating the ensemble of partitions [42], [49], [51], [76], [86], [124], [133].”
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions (2002, methodology): “Their average is then used to recluster the objects to yield consensus. as it avoids trivial partitions (Karypis et al., 1999, Strehl and Ghosh, 2002b).”
A survey on ensemble learning (2019, background): “The related work can date back to Strehl and Ghosh’s research [112].”
Clustering with Bregman Divergences (2005, methodology): “The quality of the clustering was measured in terms of the normalized mutual information(6) [24] between the predicted clusters and the original clusters and the results were averaged over 10 trials.”
A generalized maximum entropy approach to bregman co-clustering and matrix approximation (2004, methodology): “Performance is evaluated by the normalized mutual information of the document clusters with the true labels of the documents (Strehl and Ghosh, 2002).”