362 citations · 612 across the 28 of their papers we have counts for
30 papers
Some issues in robust clustering
Christian Hennig
Some key issues in robust clustering are discussed with focus on Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outl…
Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
Iven Van Mechelen, Christian Hennig, Henk A. L. Kiers
The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster-analytic methods being studied and developed in discrete mathematics, nu…
Clustering of football players based on performance data and aggregated clustering validity indexes
Serhat Akhanli, Christian Hennig
We analyse football (soccer) player performance data with mixed type variables from the 2014-15 season of eight European major leagues. We cluster these data based on a tailor-made…
A comparison of different clustering approaches for high-dimensional presence-absence data
Gabriele d'Angella, Christian Hennig
Presence-absence data is defined by vectors or matrices of zeroes and ones, where the ones usually indicate a "presence" in a certain place. Presence-absence data occur for example…
Parameters not empirically identifiable or distinguishable, including correlation between Gaussian observations
Christian Hennig
Note: Accepted version, published in Statistical Papers, https://doi.org/10.1007/s00362-023-01414-3. It is shown that some theoretically identifiable parameters cannot be empirical…
Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
Javier Espinosa-Brito, Christian Hennig
The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraint…
Validation of cluster analysis results on validation data: A systematic framework
Theresa Ullmann, Christian Hennig, Anne-Laure Boulesteix
Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To judge the quality of a clustering result, diff…
An empirical comparison and characterisation of nine popular clustering methods
Christian Hennig
Nine popular clustering methods are applied to 42 real data sets. The aim is to give a detailed characterisation of the methods by means of several cluster validation indexes that…
An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture based clustering
Christian Hennig, Pietro Coretto
We introduce a new approach to deciding the number of clusters. The approach is applied to Optimally Tuned Robust Improper Maximum Likelihood Estimation (OTRIMLE; Coretto and Henni…
Probability Models in Statistical Data Analysis: Uses, Interpretations, Frequentism-As-Model
Christian Hennig
Note: Published now as a chapter in "Handbook of the History and Philosophy of Mathematical Practice" (Springer Nature, editor B. Sriraman, https://doi.org/10.1007/978-3-030-19071-…
Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes
Serhat Emre Akhanli, Christian Hennig
A key issue in cluster analysis is the choice of an appropriate clustering method and the determination of the best number of clusters. Different clusterings are optimal on the sam…
Minkowski distances and standardisation for clustering and classification of high dimensional data
Christian Hennig
There are many distance-based methods for classification and clustering, and for data with a high number of dimensions and a lower number of observations, processing distances is c…
Clustering with the Average Silhouette Width
Fatima Batool, Christian Hennig
The Average Silhouette Width (ASW; Rousseeuw (1987)) is a popular cluster validation index to estimate the number of clusters. Here we address the question whether it also is suita…
Initialization methods for optimum average silhouette width clustering
Fatima Batool
A unified clustering approach that can estimate number of clusters and produce clustering against this number simultaneously is proposed. Average silhouette width (ASW) is a widely…
Should we test the model assumptions before running a model-based test?
M. Iqbal Shamsudheen, Christian Hennig
Statistical methods are based on model assumptions, and it is statistical folklore that a method's model assumptions should be checked before applying it. This can be formally done…
Many perspectives on Deborah Mayo's "Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars"
Andrew Gelman, Brian Haig, Christian Hennig +8
The new book by philosopher Deborah Mayo is relevant to data science for topical reasons, as she takes various controversial positions regarding hypothesis testing and statistical…
Benchmarking in cluster analysis: A white paper
Iven Van Mechelen, Anne-Laure Boulesteix, Rainer Dangl +5
Note: A revised version of this is now published. Please cite and read (it's open access): Van Mechelen, I., Boulesteix, A.-L., Dangl, R., Dean, N., Hennig, C., Leisch, F., Steinle…
Quantile-based clustering
Christian Hennig, Cinzia Viroli, Laura Anderlucci
A new cluster analysis method, -quantiles clustering, is introduced. -quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd's…
A constrained regression model for an ordinal response with ordinal predictors
Javier Espinosa, Christian Hennig
A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The pr…
Using clustering of rankings to explain brand preferences with personality and socio-demographic variables
Daniel Müllensiefen, Christian Hennig, Hedie Howells
The primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns. This paper addresses the…
Cluster validation by measurement of clustering characteristics relevant to the user
Christian Hennig
There are many cluster analysis methods that can produce quite different clusterings on the same dataset. Cluster validation is about the evaluation of the quality of a clustering;…
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation
ShengLi Tzeng, Christian Hennig, Yu-Fen Li +1
We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type…
Recovering the number of clusters in data sets with noise features using feature rescaling factors
Renato Cordeiro de Amorim, Christian Hennig
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian…
Beyond subjective and objective in statistics
Andrew Gelman, Christian Hennig
We argue that the words "objectivity" and "subjectivity" in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections…