A Stability Based Method for Discovering Structure in Clustered Data

By Asa Ben-Hur, Andre´ Elisseeﬀ, Isabelle M Guyon. Pacific Symposium on Biocomputing, 2001

679 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/825bf4fc7aacb4a202a1095abd7bc036e91f4b8a

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Citations in the corpus

Comparing clusterings---an information based distance (2007, methodology): “By approaching the relationship between two clusterings from the point of view of the information exchange – loss and gain – between them, we are exploiting once again the intuitive appeal of information theoretic concepts.”
Support Vector Clustering (2002, methodology): “These contours were interpreted by us as cluster boundaries in Ben-Hur et al. (2000). Here we discuss in detail a method which allows for a systematic search for clustering solutions without making assumptions on their number or shape, first introduced in Ben-Hur et al. (2001). In our Support Vector”
An extensive comparative study of cluster validity indices (2013, background): “Another more recent approach is the stability based validation [16] [17] which is not model dependant and does not require any assumption of compactness.”
Clustering trees: a visualization for evaluating clusterings at multiple resolutions (2018, methodology): “For example, the model explorer algorithm subsamples a dataset multiple times, clusters each subsampled dataset at various resolutions, and then calculates a similarity between clusterings at the same resolution to give a distribution of similarities that can inform the choice of resolution [10].”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “It has long been proposed to select the number of clusters in order to maximize the stability of the clustering, e.g. (Jain, 1987; Ben-Hur et al., 2002), yet the way the stability was exactly measured varied across study, see (Jain, In Press) for a review.”
Stability-Based Validation of Clustering Solutions (2004, methodology): “Only the method by Levine and Domany (2001) and Ben-Hur et al. (2002) estimate k = 3 among other number of clusters.”
Computational cluster validation in post-genomic data analysis (2005, background): “In order to assess the degree of stability of a partitioning, several papers (Ben-Hur et al., 2002; Levine and Domany, 2001) repeatedly draw overlapping subsamples of the same dataset (the individual subsamples are drawn without replacement).”
Regularized k-means clustering of high-dimensional data and its asymptotic consistency (2012, methodology): “To optimally balance the trade-off between model fitting and sparsity, a model selection criterion is developed based on the clustering stability in [3, 23].”
A tutorial on spectral clustering (2007, background): “…over information-theoretic criteria (Still and Bialek, 2004), the gap statistic (Tibshirani, Walther, and Hastie, 2001), to stability approaches (Ben-Hur, Elisseeff, and Guyon, 2002; Lange, Roth, 10 Histogram of the sample 10 Histogram of the sample 6 Histogram of the sample Braun, and Buhmann,…”
Clustering Stability: An Overview (2010, methodology): “[5] or Bertoni and Valentitni [6] for details.”
Comparing clusterings: an axiomatic view (2005, methodology): “The clustering literature contains quite a number of criteria for comparing clusterings: the Rand index (Rand, 1971), the Jaccard index (Ben-Hur et al., 2002), the Folwkes-Mallows index (Fowlkes & Mallows, 1983), the Huber and Arabie indices (Hubert & Arabie, 1985), the Mirkin metric (Mirkin, 1996),”