C. Tortora, A. Punzo, and B.C. Franczak. Handling skewness and directional tails in model-based clustering. Stat Papers 66, 114, 2025. link
C. Tortora. Evaluating methods for addressing skewness in clustering: a focus on generalized hyperbolic mixture models. Journal of Statistical Computation and Simulation, 95(12), 2643–2658, 2025. link
D. Sobti, M. Agumbe Suresh, C. Tortora, and V. Viswanathan. Domain-Specific Aspect Extraction for Product Design. In Companion Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 2754–2763, 2025. link
H. Tong and, C. Tortora. MixtureMissing: An R Package for Robust and Flexible Model-Based Clustering with Incomplete Data. Journal of Statistical Software, Accepted, 2025
C. Tortora and F. Palumbo FPDclustering: a comprehensive R package for probabilistic distance clustering based methods. Computational Statistics, 40(2) 1123-1146 2025. link.
C. Tortora, B.C. Franczak, L. Bagnato, A. Punzo. A Laplace-based model with flexible tail behavior. Computational statistics and data analysis, (192)107909, 2024. link
H. Tong and, C. Tortora. Missing values and directional outlier detection in model-based clustering. Journal of Classification, 41:480-513, 2024. link
A. Zingone, C.Tortora, D. D'alelio, F. Margiotta, D. Sarno. Assembly rules vary seasonally in stable phytoplankton associations of the Gulf of Naples (Mediterranean Sea), Marine Ecology, 44(3) e12730.2023. link
M.L. Wallace, L. McTeague, J.L. Graves, N. Kissel, C. Tortora, B. Wheeler, S. Iyengar. Quantifying Distances Between Non-Elliptical Clusters to Enhance the Identification of Meaningful Emotional Reactivity Subtypes. Data Science in Science. 1(1): 34-59, 2022. link
C. Tortora and F. Palumbo. Clustering mixed-type data using a probabilistic distance algorithm, Applied Soft Computing, 130 109704, 2022. link
J. Walker, C. Poliziani; C.Tortora, J. Schweizer, F. Rupi. Nonparametric Regression Analysis of Cyclist Waiting Times across Three Behavioral Typologies. ISPRS Int. J. Geo-Inf. 2022, 11(3), 169 2022. link
F. Mbuga and, C. Tortora. Spectral Clustering of Mixed-Type Data. Stats, 5(1) 2022. link
H. Tong and, C. Tortora. Model-based clustering and outlier detection with missing data. Advances in data analysis and classification 16(1), 5-30, 2022. link
L. Tran and C. Tortora. How Many Clusters Are Best? Investigating Model Selection in Robust Clustering. In JSM Proceedings, Statistical Learning and Data Science Section. Alexandria, VA: American Statistical Association. 1159-1180, 2021. link
W. Maciejewski, C.Tortora, and J. Bragelman. Beyond Skill: Students' Dispositions Towards Math. Journal of Developmental Education 44 (2), 18-25 2021. link
C. Poliziani, F. Rupi, F. Mbuga, J. Schweizer, and C. Tortora Categorizing three active cyclist typologies by exploring patterns on a multitude of GPS crowdsourced data attributes. Research in transportation, business, and Management, 40 100572, 2021. link
C. Tortora, R. P. Browne, A. ElSherbiny, B. C. Franczak, and P. D. McNicholas. Model-Based Clustering, Classification, and Discriminant Analysis using the Generalized Hyperbolic Distribution: MixGHD R package, Journal of Statistical Software 98(3) 1-24, 2021. link.
C. Tortora, P.D. McNicholas, and F. Palumbo. A probabilistic distance clustering algorithm using Gaussian and Student-t multivariate density distributions, F. SN COMPUT. SCI. 1: 65, 2020. link
A. Punzo and C. Tortora. The multiple scaled contaminated normal distribution and its application in model-based clustering, Statistical Modelling, 21(4) 332-358 2019. link
C. Tortora, B.C. Franczak, R.P. Browne, and P.D. McNicholas. A Mixture of Coalesced Generalized Hyperbolic Distributions, Journal of Classification, 36(1) 26-57, 2019. [link]
J. Fitch, N. Khan, and C. Tortora. Back Pain: A Spectral Clustering Approach, Archives of Data Science, series B, 1(1) 1-16 2019. [link]
L. Phan, H. Liu, and C. Tortora. K-means Clustering on Multiple Correspondence Analysis Coordinates, Archives of Data Science, series B, 1(1) 1-17, 2019. [link]
F. Liu, S. Gupta, and C. Tortora. Cluster Correspondence Analysis and Reduced K-Means: A Two-Step Approach to Cluster Low Back Pain Patients, Archives of Data Science, series B, 1(1) 1-29, 2019. [link]
C.Tortora, M.Gettler Summa, M.Marino, and F.Palumbo. Factor probabilistic distance clustering (FPDC): a new clustering method. Advances in data analysis and classification 10(4), 441-464, 2016. [link]
C.Tortora, P.D. McNicholas, and R.P. Browne. Mixtures of Generalized Hyperbolic Factor Analyzers. Advances in data analysis and classification 10(4) p.423-440, 2016. [link]
B. C. Franczak, C. Tortora, R. P. Browne, and P. D. McNicholas. Mixtures of skewed distributions with hypercube contours. Pattern Recognition Letters 58 p.69-76 2015. [link]
A.C.M. Gaudin, T.N. Tolhurst, A.P. Ker, K. Janovicek, C. Tortora, R.C. Martin and W. Deen. Increasing Crop Diversity Mitigates Weather Variations and Improves Yield Stability. PLOS ONE 10(2) 2015:e0113261.[link]
M.Marino and C.Tortora. A comparison between k-means and support vector clustering of categorical data. Statistica applicata-Italian Journal of Applied Statistics, 21(1) :5–16 ISSN 1125–1964, 2009.[link]
N. Murugesan, I. Cho, and C. Tortora. Benchmarking in cluster analysis: a study on Spectral Clustering, DBSCAN, and K-means. In Studies in Data Analysis and Rationality in a Complex World, Chadjipadelis et al (eds.), 175-185 2021. [link]
J. Jimeno, M. Roy, and C. Tortora. Clustering Mixed-Type Data: a benchmark study on KAMILA and K-prototypes. In Studies in Data Analysis and Rationality in a Complex World, Chadjipadelis et al (eds.), 83-91 2021. [link]
A.Molan, N. Murugesan, A. Shams, C. Tortora, F. Rahman, J. Loh, A. Pande. Evaluation of Coordinated Ramp Metering (CRM) Implemented by Caltrans, Mineta Transportation Institute, May 2020 [link]
A. W. Agrawal, A. Loukaitou-Siders, C. Tortora, and Y. Hu. Crime and Harassment on Public Transportation: A Survey of SJSU Students Set in International Context, Mineta Transportation Institute, May 2020 [link]
C. Rainey, C. Tortora and F.Palumbo. A parametric version of probabilistic distance clustering. In: Greselin F., Deldossi L., Bagnato L., Vichi M. (eds) Statistical Learning of Complex Data. CLADAG 2017. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham, 33-43 2019 [link]
C.Tortora M.Marino and G.Scepi. Factor PD-co-clustering. In Studies in Theoretical and Applied Statistics, Alleva et al. (Eds) 111-119 2016.[link]
C.Tortora and M.Marino. Robustness and stability analysis of factor PD-clustering on large social datasets. Analysis and Modeling of Complex Data in Behavioural and Social Sciences Vicari et al. (eds) 283-281 2014.[link]
C.Tortora, M.Gettler Summa, and F.Palumbo. Factor pd-clustering. In Algorithms from and for Nature and Life, Studies in Classification, Data Analysis, and Knowledge Organization 115-123 2013.[link]
L .Iandoli, V. Lanzetta, F.Palumbo, C. Ponsiglione, C.Tortora and G.Zollo. The Implementation of a Self-Sustaining Regional Innovation System .In RENT XXVI Research in Entrepreneurship and Small Business, Entrepreneurship and Creation of Wealth for Economies, Organization and People, ISSN 2219-5572 , 2012.
M.Marino, F.Palumbo, and C.Tortora. Clustering in feature space for interesting pattern identification of categorical data. Advanced Statistical Methods for the Analysis of Large Data-Sets, Springer Berlin Heidelberg, 13-22, 2012.[link]
A.W. Agrawal , A. Loukaitou-Sideris., Y. Hu, and C. Tortora. San Jose Case Study in Transit Crime and Sexual Violence Globally: A Tale of 18 Cities 2019 link
F.Bifulco, G.Capaldo, A.Capasso, M.Caputo, M. D’Amore, F.Palumbo, P.Stampacchia, R.Vona, G.Zollo, E.Imperiale, G.Orefice, C. Ponsiglione, E.Casati, E.De Crescenzo, V. Lanzetta e C.Tortora. Innovation Scoreboard della regione Campania. Technical Report, Campania e Innovazione S.p.a. 2012.[link]
A. Iodice D’Enza, C. Tortora, and F. Palumbo. Spectral clustering of mixed data via association-based distance. In book of short papers of Scientific meeting of the Italian Statistical Society, 2023.
C. Rainey, C. Tortora and F. Palumbo. Probabilistic distance algorithm generalization to Student's t mixtures. In book of short papers of JSC-CLADAG 17, 2017.
C.Tortora, P. D. McNicholas and R.P. Browne, Mixture of generalized hyperbolic factor analyzers. In book of short papers of CLADAG 2013.
C.Tortora and M.Marino. Robustness and stability analysis of factor pd-clustering on large social datasets. In book of short papers of JSC-CLADAG 2012 ISBN 978- 88-6129-916-0, 2012.
M.Marino, G.Scepi, and C.Tortora. Factor pd-co-clustering in official statistics. 46th Scientific Meeting of the Italian Statistical Society. Rome, 20-22 June 2012, (ISBN 978-88-6129-882-8), 2012.
C.Tortora, F.Palumbo, and M.Gettler Summa. Cd-clustering. Book of abstract, The 6th CARME conference, 80, 2011.
M. Gettler Summa, F.Palumbo, and C.Tortora. Etude comparée de classifications sur matrices très creuses et de grandes dimensions. Résumés des 42es Journées de la Soc. Francaise de Statistique, Marseille, France, May 24 - 28, 52, 2010.
M.Marino, F.Palumbo, and C.Tortora. Clustering in feature space for interesting pattern identification of categorical data. Book of short papers, Convegno intermedio SIS, (ISBN 978-88-6129-425-7) : 459–462, 2009.