Stability-Based Validation of Clustering Solutions

By Tilman Lange, Volker Roth, Mikio L. Braun, Joachim M. Buhmann. Neural Computation, 2004

554 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/890e3eb158d5bdfa3042869962d5838ae81be7fe

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Citations in the corpus

Clustering Stability: An Overview (2010, methodology): “• Compare clusterings of disjoint subsamples [12, 15].”
A tutorial on spectral clustering (2007, methodology): “In the machine learning community, spectral clustering has been made popular by the works of Shi and Malik (2000), Ng et al. (2002), Meila and Shi (2001), and Ding (2004).”
Computational cluster validation in post-genomic data analysis (2005, methodology): “A slightly different approach has been taken in [10, 25, 46 , 69].”
A random forest based approach for predicting spreads in the primary catastrophe bond market (2020, methodology): “Various ways to measure the stability of algorithmic results have been presented in Turney (1995), Lange et al. (2004), Ntoutsi et al.”
How Many Clusters? An Information-Theoretic Perspective (2003, background): “Roth, Lange, Braun, and Buhmann (2004) quantify the goodness of the clustering with a resampling approach.”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “There is also a wide range of machine learning techniques that seek to estimate the number of modules in a clustering problem, including Bayesian information criterion (BIC; Fraley & Raftery 1998) and stability analysis (Lange et al. 2004).”
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models (2010, methodology): “A new approach to model selection, based on model stability, has recently generated some interest in the literature [8].”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “Different cross-validation protocols can be used, the two most widely explored being 2-fold cross-validation [15], [31] and bootstrap resampling or subsampling [7], [9], [22], [23], [24], [27], [28].”
Data clustering: 50 years beyond K-means (2008, unknown): “The notion of cluster stability (Lange et al., 2004) is appealing as an internal stability measure.”
A CLUE for CLUster Ensembles (2005, unknown): “In addition, method "majority" obtains a consensus hierarchy from an extension of the majority consensus tree of Margush and McMorris (1981), which minimizes L(U) = ∑ b wbd(Ub, U) over all ultrametrics U , where d is the symmetric difference dissimilarity.”
Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data (2011, methodology): “In our experience, both basic model selection schemes such as BIC (Schwarz, 1978) and computationally intensive resampling methods such as that of Lange et al. (2004) yield monotonically increasing measures for the goodness of the finite mixture model up to cluster numbers in several hundreds.”
Characteristic-Based Clustering for Time Series Data (2006, methodology): “We can then use the stability of clustering (as defined in Lange et al. (2004)) as a measure of feature quality.”
An Unsupervised Aspect-Sentiment Model for Online Reviews (2010, methodology): “approach (Levine and Domany, 2001; Lange et al., 2004; Niu et al., 2007) is to use a cluster validation procedure.”