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Advances in Data Analysis and Classification, Volume 14
Volume 14, Number 1, March 2020
- Editorial for ADAC issue 1 of volume 14 (2020). 1-4
- Nan-Ting Liu, Feng-Chang Lin, Yu-Shan Shih
:
Count regression trees. 5-27 - Ahmad Ali Abin, Mohammad Ali Bashiri, Hamid Beigy
Learning a metric when clustering data points in the presence of constraints. 29-56 - Ana Helena Tavares
Clustering genomic words in human DNA using peaks and trends of distributions. 57-76 - Elson Claudio Correa Moraes, Danton Diego Ferreira
Data clustering based on principal curves. 77-96 - Zardad Khan, Asma Gul, Aris Perperoglou, Miftahuddin Miftahuddin, Osama Mahmoud, Werner Adler, Berthold Lausen
Ensemble of optimal trees, random forest and random projection ensemble classification. 97-116 - Jorge Caiado
A fragmented-periodogram approach for clustering big data time series. 117-146 - Johannes Blömer, Sascha Brauer, Kathrin Bujna, Daniel Kuntze:
How well do SEM algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models. 147-173 - Víctor Blanco
Optimal arrangements of hyperplanes for SVM-based multiclass classification. 175-199 - Alexander Katzur, Udo Kamps:
Classification using sequential order statistics. 201-230
Volume 14, Number 2, June 2020
- Christophe Biernacki, Luis Angel García-Escudero, Salvatore Ingrassia:
Special issue on "Innovations on model based clustering and classification". 231-234 - Giuliano Galimberti
Seemingly unrelated clusterwise linear regression. 235-260 - Sijia Xiang
Semiparametric mixtures of regressions with single-index for model based clustering. 261-292 - Keefe Murphy
Gaussian parsimonious clustering models with covariates and a noise component. 293-325 - Andrea Cappozzo
A robust approach to model-based classification based on trimming and constraints. 327-354 - Semhar Michael
Mixture modeling of data with multiple partial right-censoring levels. 355-378 - Shuchismita Sarkar
Gaussian mixture modeling and model-based clustering under measurement inconsistency. 379-413 - Michael P. B. Gallaugher, Paul D. McNicholas
Mixtures of skewed matrix variate bilinear factor analyzers. 415-434 - Jorge M. Arevalillo
Data projections by skewness maximization under scale mixtures of skew-normal vectors. 435-461 - Nathan Cunningham
ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification. 463-484 - Riccardo Rastelli
A stochastic block model for interaction lengths. 485-512
Volume 14, Number 3, September 2020
- Maurizio Vichi, Andrea Cerioli, Hans A. Kestler
Editorial for ADAC issue 3 of volume 14 (2020). 513-515 - Gerhard Tutz
Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model. 517-542 - Khadidja Henni
Is-ClusterMPP: clustering algorithm through point processes and influence space towards high-dimensional data. 543-570 - Armin Rauschenberger
Sparse classification with paired covariates. 571-588 - Lukás Malec
Connecting the multivariate partial least squares with canonical analysis: a path-following approach. 589-609 - Vasileios Maroulas
A stable cardinality distance for topological classification. 611-628 - Rosaria Lombardo
Familywise decompositions of Pearson's chi-square statistic in the analysis of contingency tables. 629-649 - Alba M. Franco Pereira
Rank tests for functional data based on the epigraph, the hypograph and associated graphical representations. 651-676 - Ikram Chaabane
Enhancing techniques for learning decision trees from imbalanced data. 677-745
Volume 14, Number 4, December 2020
- Rainer Schlittgen, Marko Sarstedt
Data generation for composite-based structural equation modeling methods. 747-757 - Laura Anderlucci
Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data. 759-770 - Cristina Davino
On the use of quantile regression to deal with heterogeneity: the case of multi-block data. 771-784 - Stanislav Vojír
Editable machine learning models? A rule-based framework for user studies of explainability. 785-799 - Yanou Ramon
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C. 801-819 - Mark Gromowski
A process framework for inducing and explaining Datalog theories. 821-835 - Carlo Cavicchia
The ultrametric correlation matrix for modelling hierarchical latent concepts. 837-853 - Adam Sagan
SEM-Tree hybrid models in the preferences analysis of the members of Polish households. 855-869 - Ludwig Lausser, Robin Szekely, Hans A. Kestler
Chained correlations for feature selection. 871-884 - Cornelia Fuetterer
Adapted single-cell consensus clustering (adaSC3). 885-896 - Ana Isabel Aguilera
Automatic gait classification patterns in spastic hemiplegia. 897-925 - Atsuho Nakayama
Predicting brand confusion in imagery markets based on deep learning of visual advertisement content. 927-945 - Kamila Migdal Najman
The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers. 947-982
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