A Survey of Clustering Ensemble Algorithms

By Sandro Vega-Pons, José Ruiz-Shulcloper. International journal of pattern recognition and artificial intelligence, 2011

629 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0d1b7d01fb2634b6160a96bbdd73f918ed3859cb

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Citations in the corpus

A Comprehensive Survey of Clustering Algorithms (2015, background): “The consensus functions can be divided into the following 9 categories, summarized in Table 16: For more detailed information about this kind of clustering algorithms, you can refer to [95].”
Ensemble learning: A survey (2018, methodology): “A thorough review of other traditional clustering model integration approaches can be found in Ghaemi et al. (2009) and Vega-Pons and Ruiz-Shulcloper (2011) surveys.”
A Survey on Multi-View Clustering (2017, neutral): “Ensemble clustering [117] (also named consensus clustering or aggregation of clustering) is to reconcile clustering information about the same data set coming from different sources or from different runs of the same clustering method to find a single consensus clustering that is a better fit in som”
Ensemble deep learning in bioinformatics (2020, methodology): “Like supervised ensemble learning, conventional unsupervised ensemble learning, such as ensemble clustering 11 , also relies on the generation and integration of base models (Fig.”
Locally Weighted Ensemble Clustering (2016, methodology): “Typically, there are two major directions in ensemble learning, that is, ensemble classifiers [41]–[43] and ensemble clustering [44].”
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].”
Ensemble deep learning: A review (2021, methodology): “There exist multiple surveys in the literature which mainly focus on the review of ensemble learning like learning of ensemble models in classification problems [16, 17, 18, 19], regression problems [20, 21] and clustering [22].”