Alfonso Iodice D'Enza
Cited by
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The endoscopic endonasal approach for the management of craniopharyngiomas: a series of 103 patients
LM Cavallo, G Frank, P Cappabianca, D Solari, D Mazzatenta, A Villa, ...
Journal of neurosurgery 121 (1), 100-113, 2014
Sellar repair with fibrin sealant and collagen fleece after endoscopic endonasal transsphenoidal surgery
P Cappabianca, LM Cavallo, V Valente, I Romano, AI D'Enza, F Esposito, ...
Surgical neurology 62 (3), 227-233, 2004
Principal component analysis
M Greenacre, PJF Groenen, T Hastie, AI d’Enza, A Markos, E Tuzhilina
Nature Reviews Methods Primers 2 (1), 100, 2022
Distance‐based clustering of mixed data
M Van de Velden, A Iodice D'Enza, A Markos
Wiley Interdisciplinary Reviews: Computational Statistics 11 (3), e1456, 2019
Cluster correspondence analysis
M Van de Velden, AI D’Enza, F Palumbo
Psychometrika 82, 158-185, 2017
Endoscopic endonasal transsphenoidal removal of recurrent and regrowing pituitary adenomas: experience on a 59-patient series
LM Cavallo, D Solari, A Tasiou, F Esposito, M de Angelis, AI D'Enza, ...
World neurosurgery 80 (3-4), 342-350, 2013
The “suprasellar notch,” or the tuberculum sellae as seen from below: definition, features, and clinical implications from an endoscopic endonasal perspective
M de Notaris, D Solari, LM Cavallo, AI D'Enza, J Enseņat, J Berenguer, ...
Journal of neurosurgery 116 (3), 622-629, 2012
Beyond tandem analysis: Joint dimension reduction and clustering in R
A Markos, AI D'Enza, M van de Velden
Journal of Statistical Software 91, 1-24, 2019
Iterative factor clustering of binary data
A Iodice D’Enza, F Palumbo
Computational Statistics 28, 789-807, 2013
Multiple correspondence analysis for the quantification and visualization of large categorical data sets
AI D’Enza, M Greenacre
Advanced statistical methods for the analysis of large data-sets, 453-463, 2012
’Enza A, Markos A (2019) Distance-based clustering of mixed data
M Van de Velden, D Iodice
WIREs Comput Stat 11 (3), e1456, 0
’Enza, A., & van de Velden, M.(2019). Beyond tandem analysis: joint dimension reduction and clustering in R
A Markos, D Iodice
Journal of Statistical Software 91 (10), 1-24, 0
Exploratory data analysis leading towards the most interesting simple association rules
AI D’enza, F Palumbo, M Greenacre
Computational Statistics & Data Analysis 52 (6), 3269-3281, 2008
’Enza A, Markos A
M Van de Velden, D Iodice
Distance-based clustering of mixed data. WIREs Computational Stat 11, e1456, 2019
On joint dimension reduction and clustering of categorical data
A Iodice D’Enza, M Van de Velden, F Palumbo
Analysis and modeling of complex data in behavioral and social sciences, 161-169, 2014
’Enza A, Van de Velden M (2019). clustrd: Methods for Joint Dimension Reduction and Clustering
A Markos, D Iodice
R package version 1 (0), 0
Special feature: dimension reduction and cluster analysis
M van de Velden, AI D’Enza, M Yamamoto
Behaviormetrika 46, 239-241, 2019
The idm package: incremental decomposition methods in R
AI D'Enza, A Markos, D Buttarazzi
Journal of Statistical Software 86, 1-24, 2018
Low-dimensional tracking of association structures in categorical data
A Iodice D’Enza, A Markos
Statistics and Computing 25, 1009-1022, 2015
Incremental Generalized Canonical Correlation Analysis
A Markos, AI D’Enza
Analysis of Large and Complex Data, 185-194, 2016
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