Cluster Validation by Prediction Strength

By Robert Tibshirani, Guenther Walther. 2005

651 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5e3c910f6ab10497e125cf04c05ef3bb8a5bdd59

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Citations in the corpus

Is My Network Module Preserved and Reproducible? (2011, methodology): “, the Rand index [53] or prediction based statistics [26,27].”
Contextual Outlier Interpretation (2017, methodology): “To determine the number of clusters L in Ci , we adopt the measure of prediction strength [47] which shows good performance even when dealing with high-dimensional data.”
Computational cluster validation in post-genomic data analysis (2005, methodology): “The methods described in [4, 7, 10, 25, 42, 46, 48, 49, 53, 69] all employ the concept of self-consistency, that is, the idea that a”
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics (2010, methodology): “, the voltage values at all electrodes at that time point) were extracted and submitted to a modified spatial cluster analysis using the atomize-agglomerate hierarchical clustering (AAHC) method (75) to identify the most dominant map topographies (10).”
Stability-Based Validation of Clustering Solutions (2004, neutral): “Although not explicitly formulated in a probabilistic way, the general validation methodology proposed in Yeung, Haynor, and Ruzzo (2001) as well as the Gap Statistic ( Tibshirani, Walther, & Hastie, 2001; see section 3) are model dependent, since both methods assume compact clusters tightly packed”
A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets (2013, methodology): “We chose the number of clusters and quality of the resulting clusters by maximizing the prediction strength (PS) [13] and silhouette index (SI) [14].”
Classification Approaches in Neighborhood Research: Introduction and Review (2011, methodology): “The general term for such methods is cluster number estimation measures; they include among other approaches cross-validation techniques (cf. Dudoit and Fridlyand, 2002; Tibshirani and Walther, 2005; Fang et al., 2007) and cluster validity indices (Maulik and Bandyopadhyay, 2002; Kryszczuk and Hurle”
Microstates in Resting-State EEG: Current Status and Future Directions (2014, methodology): “The field would benefit from standardization of the many technical aspects of the method that have been developed over the last two decades (Wackermann et al., 1993, Pascual-Marqui et al., 1995, Tibshirani and Walther, 2005, Britz et al., 2010, Yuan et al., 2012).”
Topographic ERP Analyses: A Step-by-Step Tutorial Review (2008, methodology): “on k-means clustering [49], and the other on hierarchical clustering [66] that has been renamed ‘‘AAHC’’ for Atomize”
Cluster-wise assessment of cluster stability (2007, background): “Examples are Ben-Hur et al. (2002), Bryan (2004), Dudoit and Fridlyand (2002), Grün and Leisch (2004), Lange et al. (2004), Monti et al. (2001) and Tibshirani and Walther (2005).”
Clustering of time-course gene expression data using a mixed-effects model with B-splines (2003, positive): “Methods such as those based on the predictive strength or re-sampling (Tibshirani et al., 2001) can be similarly adapted for our proposed mixed-effects models.”
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms (2004, methodology): “The next section describes evaluation graphs, followed by a description of the L method.”
A Framework for Feature Selection in Clustering (2010, neutral): “…scope of this paper, and has been discussed extensively in the literature for standard K -means clustering; we refer the interested reader to Milligan and Cooper (1985), Kaufman and Rousseeuw (1990), Tibshirani, Walther, and Hastie (2001), Sugar and James (2003), and Tibshirani and Walther (2005).”
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data (2008, methodology): “For validation of clusters, Tibshirani, Walther, Bostein, and Brown (2005) proposed using resampling techniques to evaluate the prediction strength of different clusters.”
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (2003, methodology): “…perturbations of the original data set, so as to assess the stability of the clustering results with respect to sampling variability (Ben-Hur, Elisseeff, & Guyon, 2002; Bhattacharjee et al., 2001; Dudoit & Fridlyand, 2002; Jain & Moreau, 1988; Levine & Domany, 2001; Tibshirani et al., 2001a).”
Automated Marketing Research Using Online Customer Reviews (2011, neutral): “There are several general, statistical approaches for initializing k including the Gap (Tibshirani and Walther 2005) and KL (Krzanowski and Lai 1988) statistics.”