| Principal component analysis M Greenacre, PJF Groenen, T Hastie, AI d’Enza, A Markos, E Tuzhilina Nature Reviews Methods Primers 2 (1), 100, 2022 | 416 | 2022 |
| 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 | 261 | 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 | 122 | 2004 |
| 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 | 94 | 2019 |
| Cluster correspondence analysis M Van de Velden, AI D’Enza, F Palumbo Psychometrika 82, 158-185, 2017 | 89 | 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 | 78 | 2013 |
| 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 | 73 | 2019 |
| 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 | 65 | 2012 |
| Iterative factor clustering of binary data A Iodice D’Enza, F Palumbo Computational Statistics 28, 789-807, 2013 | 37 | 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 | 32 | 2012 |
| ’Enza A, Markos A (2019) Distance-based clustering of mixed data M Van de Velden, D Iodice Wiley Interdisciplinary Reviews: Computational Statistics 11 (3), e1456, 0 | 21 | |
| 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 | 11 | 2008 |
| ’Enza A, Markos A M Van de Velden, D Iodice Distance‑based clustering of mixed data. WIREs Comput Stat 11 (3), e1456, 2019 | 10 | 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 | 10 | 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 | 10 | |
| Special feature: dimension reduction and cluster analysis M van de Velden, AI D’Enza, M Yamamoto Behaviormetrika 46, 239-241, 2019 | 9 | 2019 |
| Publisher correction: principal component analysis M Greenacre, PJF Groenen, T Hastie, AI D’Enza, A Markos, E Tuzhilina Nature Reviews Methods Primers 3 (1), 22, 2023 | 8 | 2023 |
| The idm package: incremental decomposition methods in R AI D'Enza, A Markos, D Buttarazzi Journal of Statistical Software 86, 1-24, 2018 | 8 | 2018 |
| A general framework for implementing distances for categorical variables M Van De Velden, AI D'Enza, A Markos, C Cavicchia arXiv preprint arXiv:2301.02190, 2023 | 6 | 2023 |
| Low-dimensional tracking of association structures in categorical data A Iodice D’Enza, A Markos Statistics and Computing 25, 1009-1022, 2015 | 6 | 2015 |
Angelos MarkosProfessor of Data Analysis, Democritus University of ThraceVerified email at eled.duth.gr
