Bagging to Improve the Accuracy of A Clustering Procedure

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Cluster ensembles (2011, methodology): “The concept of cumulative voting was ﬁrst introduced in Ref 46 where the authors used bagging to improve the accuracy of clustering procedure.”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “Among these are random initializations of the clustering algorithm, subsampling, or resampling the data [5], [8], [9], [13], [16], [26], [27], applying different types of clustering algorithms [1], [16], [18], [37], using subsets of features [16], [32], “weakening” the clustering algorithm [16], [34”
A CLUE for CLUster Ensembles (2005, methodology): “Alternative recent approaches to obtaining consensus partitions include “Bagged Cluster-ing” (Leisch 1999, implemented in package e1071 ), the “evidence accumulation” framework of Fred and Jain (2002), the NMI optimization and graph-partitioning methods in Strehl and Ghosh (2003a), “Bagged Clusterin”
A Mixture Model for Clustering Ensembles (2004, methodology): “A combination of partitions obtained from multiple bootstrap samples is implemented in [8, 9].”
Solving cluster ensemble problems by bipartite graph partitioning (2004, methodology): “Our experiments use two approaches, random subsampling (Dudoit & Fridlyand, 2003) and random projection (Fern & Brodley, 2003), to generate the ensembles.”