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TJ SMITH (2001)
Constructing Ultrametric and Additive Trees Based on the L1-NormJournal of Classification, 18
D STEINLEY, MJ BRUSCO (2011)
Evaluating the Performance of Model-Based Clustering: Recommendations and CautionsPsychological Methods, 16
G SOETE (1984)
Ultrametric tree representations of incomplete dissimilarity dataJournal of Classification, 1
I SULIS, M PORCU (2017)
Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class AnalysisJournal of Classification, 34
A PUNZO, PD MCNICHOLAS (2017)
Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted ModelJournal of Classification, 34
G SOETE, JD CARROLL, WS DESARBO (1987)
Least Squares Algorithms for Constructing Constrained Ultrametric and Additive Tree Representations of Symmetric Proximity DataJournal of Classification, 4
R SICILIANO, A D’AMBROSIO, M ARIA, S AMODIO (2016)
Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence RulesJournal of Classification, 33
M SRIRAM, S LEWIS (1993)
Constructing Optimal UltrametricsJournal of Classification, 10
C HENNIG, TF LIAO (2013)
How to Find an Appropriate Clustering for Mixed-Type Variables with Application to Socio-Economic StratificationJournal of the Royal Statistical Society, C, 62
AP ZUBAREV (2014)
“On Stochastic Generation of Ultrametrics in High-Dimensional Euclidean Spaces”, p-Adic NumbersUltrametric Analysis, and Applications, 6
D STEINLEY (2003)
Local Optima in K-means Clustering: What You Don't Know May Hurt YouPsychological Methods, 9
J JOSSE, M CHAVENT, B LIQUET, F HUSSON (2012)
Handling Missing Values With Regularized Iterative Multiple Correspondence AnalysisJournal of Classification, 29
N SRIRAM (1990)
Clique Optimization: A Method to Construct Parsimonious Ultrametric Trees From Similarity DataJournal of Classification, 7
D STEINLEY, MJ BRUSCO (2008)
Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight ProceduresPsychometrika, 73
PE BRADLEY (2017)
Finding Ultrametricity in Data Using TopologyJournal of Classification, 34
JH FRIEDMAN, JJ MEULMAN (2004)
Clustering Objects on Subsets of AttributesJournal of the Royal Statistical Society, B, 66
UJ DANG, A PUNZO, PD MCNICHOLAS, S INGRASSIA, RP BROWNE (2017)
Multivariate Response and Parsimony for Gaussian Cluster Weighted-ModelsJournal of Classification, 34
F-J LAPOINTE, P LEGENDRE (1991)
The Generation of Random Ultrametric Matrices Representing DendrogramsJournal of Classification, 8
V MAKARENKOV, P LEGENDRE (2001)
Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and SoftwareJournal of Classification, 18
G SOETE (1988)
OVWTRE: A Program for Optimal Variable Weighting for Ultrametric and Additive Tree FittingJournal of Classification, 5
GW MILLIGAN (1980)
An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering AlgorithmsPsychometrika, 45
D STEINLEY (2006)
Profiling Local Optima in K-means Clustering: Developing a Diagnostic TechniquePsychological Methods, 11
F MURTAGH (2004)
On Ultrametricity, Data Coding, and ComputationJournal of Classification, 21
M VRAC, L BILLARD, E DIDAY, A CHEDIN (2012)
Copula Analysis of Mixture ModelsComputational Statistics, 27
Journal of Classification 34:36 1-36 5 (2017) DOI: 10.1007/s00357- -0 1 7-92 45 -7 This third issue of Volume 34 covers a lot of ground. As usual, there are a wide range of theoretical papers that are based on a bevy of tried and true multivariate techniques. However, there are also some unique aspects to this issue that would be welcome to expand upon in future issues as it provides a healthy and vibrant variability to the Journal of Classification: (1) the first paper focused exclusively on Bayesian techniques that have appeared in some years, (2) two application papers that integrate multiple multivariate and classification approaches to solve extremely useful “real-world” problems, and (3) the first software package that has appeared in several volumes. I discuss each of these in turn, and then conclude with a short paragraph outlining a specific project that I have begun to pursue that will flesh out some of the history of the content of the Journal of Classification. The first paper in the final issue of 2017 is by A.P. Zubarev and addresses the nature of the ultrametric that can be generated from a random distribution of points in Euclidean space when the
Journal of Classification – Springer Journals
Published: Nov 10, 2017
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