Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method

By G'abor J. Sz'ekely, Maria L. Rizzo. Journal of Classification, 2005

667 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c4bc07e73bfd2ff3e2635d85a7ffd5f4096c1587

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013, methodology): “The goodness-of-fit statistic used in Matteson and James (2013) is the between-within distance (Székely and Rizzo, 2005) among adjacent segments.”
Distance covariance in metric spaces (2011, background): “[These notions were conflated in SRB and Székely and Rizzo (2005a, 2005b).]”
On Brownian Distance Covariance and High Dimensional Data (2009, neutral): “References Székely GJ, Rizzo ML. Brownian distance covariance.”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, background): “…by Lance–Williams dissimilarity update formula (Lance and Williams 1967), where the parameters can define the different agglomerative criterion (Reddy and Vinzamuri 2013; Murtagh and Contreras 2012) and its generalizations (Wishart 1969; Batagelj 1988; Jambu et al. 1989; Székely and Rizzo 2005).”
Energy distance (2016, methodology): “E-clustering Energy distance has been applied in hierarchical cluster analysis.(17) It generalizes the well-known Ward’s minimum variance method in a similar way that DISCO generalizes ANOVA.”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, methodology): “We also experimented with four agglomerative methods, including Ward’s method [60], Centroid Linkage Method [61], Average Linkage method [61], and McQuitty’s Similarity method [62].”
Energy statistics: A class of statistics based on distances (2013, methodology): “For a fairly general class of hierarchical clustering algorithms including Ward's minimum variance, an algorithm is uniquely determined by its recursive formula for updating cluster distances (see Székely and Rizzo, 2005b for details).”
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, methodology): “We also experimented with four agglomerative methods, including Ward’s method [60], Centroid Linkage Method [61], Average Linkage method [61], and McQuitty’s Similarity method [62].”
Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? (2011, methodology): “Sz´ekely and Rizzo (2005) consider higher order powers of this, in the Ward context: “Our proposed method extends Ward’s minimum variance method.”
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “The E-Divisive method combines bisection (Vostrikova 1981) with a multivariate divergence measure from Székely and Rizzo (2005).”
TESTING FOR EQUAL DISTRIBUTIONS IN HIGH DIMENSION (2004, methodology): “The above inequality has been applied to develop consistent rotationinvariant goodness-of-fit tests, a consistent test of multivariate normality (Rizzo 2002, Székely and Rizzo 2005), test for Poisson distribution (Székely and Rizzo 2004) and clustering procedure (Székely and Rizzo 2003b).”
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation (2019, methodology): “, problem (II)) and propose a test for this goodness-of-fit using the rank energy statistic which is based on the usual energy statistic (as in [141], also see [8, 139] for definitions and motivation).”