Journal of Classification

Papers
(The TQCC of Journal of Classification is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
A Comparison of Reliability Coefficients for Ordinal Rating Scales34
Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data31
Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints21
Initializing k-means Clustering by Bootstrap and Data Depth20
Comparing High-Dimensional Partitions with the Co-clustering Adjusted Rand Index17
Matrix Normal Cluster-Weighted Models17
Understanding the Adjusted Rand Index and Other Partition Comparison Indices Based on Counting Object Pairs13
Adjusted Concordance Index: an Extensionl of the Adjusted Rand Index to Fuzzy Partitions10
A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models9
MatTransMix: an R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling8
Hierarchical Means Clustering7
Comparing Boosting and Bagging for Decision Trees of Rankings6
k-Means, Ward and Probabilistic Distance-Based Clustering Methods with Contiguity Constraint6
Co-clustering of Time-Dependent Data via the Shape Invariant Model6
Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators6
High-Dimensional Clustering via Random Projections5
Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning4
Network-Based Discriminant Analysis for Multiclassification4
Multinomial Principal Component Logistic Regression on Shape Data4
Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles4
Alternative Axioms in Group Identification Problems4
On Finite Mixture Modeling of Change-point Processes3
Community Detection in Feature-Rich Networks Using Data Recovery Approach3
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering3
Imputation Strategies for Clustering Mixed-Type Data with Missing Values3
Estimating the Covariance Matrix of the Maximum Likelihood Estimator Under Linear Cluster-Weighted Models3
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