Clustering ensembles: models of consensus and weak partitions

By Alexander P. Topchy, Anil K. Jain, William F. Punch III. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

718 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/83b16b24b2996bea65e063e6233416a8f05746eb

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Citations in the corpus

Locally Weighted Ensemble Clustering (2016, methodology): “[21] cast the median partition problem into a maximum likelihood problem and approximately solve it by the EM algorithm.”
An Evolutionary Approach to Multiobjective Clustering (2007, methodology): “The resulting solutions are then combined into an ensemble clustering using graph-based approaches [9], expectation maximization [36] or co-association methods [36].”
A Survey of Clustering Ensemble Algorithms (2011, methodology): “Despite the simplicity of this kind of algorithms, Topchy et al. 78 showed that weak clustering algorithms are capable of producing high quality consensus clusterings in conjunction with a proper consensus function.”
OpenEnsembles: A Python Resource for Ensemble Clustering (2018, methodology): “In the current version of OpenEnsembles, we have implemented majority vote as proposed by Ana Fred (Fred, 2001) and mixture models as proposed by Topchy et al. (Topchy et al., 2005).”
Multitask Bregman clustering (2010, background): “Transfer learning [9,20–22] attempts to improve learning performance on a target data set by utilizing auxiliary data sets.”
Community detection in networks: A user guide (2016, background): “Consensus clustering (Goder and Filkov, 2008; Strehl and Ghosh, 2002; Topchy et al., 2005) is based on this idea.”
Convex Discriminative Multitask Clustering (2013, methodology): “The most similar work with MTC in machine learning and data mining is cluster ensemble [54], [55], [56], [57], [58], [59], [60], [61], [62].”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “Table 16 Consensus functions Name Typical algorithm and application Based on co-association matrix [85] Based on graph partition CSPA, HGPA and MCLA [86] Based on relabeling and voting VM [88] Based on the hybrid model [89] Based on information theory [90] Based on genetic algorithm HCE [91] Based o”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “One possibility to reduce the problem of the failure of a clustering algorithm in scenarios where the clustering criterion employed is inappropriate is the use of ensemble techniques to integrate the results of a variety of different clustering methods [60], [61].”