362 citations · 612 across the 28 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
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…
Clustering strategy and method selection
Christian Hennig
This paper is a chapter in the forthcoming Handbook of Cluster Analysis, Hennig et al. (2015). For definitions of basic clustering methods and some further methodology, other chapt…
Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters
Christian Hennig, Chien-Ju Lin
There are two notoriously hard problems in cluster analysis, estimating the number of clusters, and checking whether the population to be clustered is not actually homogeneous. Giv…
Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering
Pietro Coretto, Christian Hennig
The two main topics of this paper are the introduction of the "optimally tuned improper maximum likelihood estimator" (OTRIMLE) for robust clustering based on the multivariate Gaus…
Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering
Pietro Coretto, Christian Hennig
The robust improper maximum likelihood estimator (RIMLE) is a new method for robust multivariate clustering finding approximately Gaussian clusters. It maximizes a pseudo-likelihoo…