Model selection strategies for determining the optimal number of overlapping clusters in additive overlapping partitional clustering

DOI10.1007/s00357-021-09409-1OpenAlexW4206058336MaRDI QIDQ2169873

Julian Rossbroich, Jeffrey Durieux, Tom Frans Wilderjans

Publication date: 30 August 2022

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-021-09409-1

zbMATH Keywords

model selectionAICBICoverlapping clusteringadditive profile clusteringADPROCLUSchoosing the number of clustersCHull

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

Optimal band selection using evolutionary machine learning to improve the accuracy of hyper-spectral images classification: a novel migration-based particle swarm optimization

Uses Software

• Silhouettes
• AS 136
• CHull
• ADPROCLUS

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Algorithm AS 136: A K-Means Clustering Algorithm
• Lowdimensional additive overlapping clustering
• Additive biclustering: a comparison of one new and two existing ALS algorithms
• Graph-based data clustering with overlaps
• On the advantage of overlapping clusters for minimizing conductance
• A sequential fitting procedure for linear data analysis models
• Algorithms for additive clustering of rectangular data tables
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• The method of principal clusters
• Estimating the dimension of a model
• An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models
• On the complexity of additive clustering models
• Interval set clustering of web users with rough \(K\)-means
• The \(k\)-weak hierarchical representations: An extension of the indexed closed weak hierarchies
• Akaike's information criterion and recent developments in information complexity
• Cross-validation methods
• Generalized functional linear models
• ECM: An evidential version of the fuzzy \(c\)-means algorithm
• An overlapping cluster algorithm to provide non-exhaustive clustering
• The local minima problem in hierarchical classes analysis: an evaluation of a simulated annealing algorithm and various multistart procedures
• Consistent selection of the number of clusters via crossvalidation
• A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering
• Model Selection and Model Averaging
• Introduction to Information Retrieval
• Regression and time series model selection in small samples
• Well-Separated Clusters and Optimal Fuzzy Partitions
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finding the Number of Clusters in a Dataset
• Community structure in social and biological networks
• A Review of Hierarchical Classification
• 10.1162/1532443041827943
• Mixture modelling for cluster analysis
• Two-mode clustering methods: astructuredoverview
• Two Variants of the OKM for Overlapping Clustering
• Overlapping community detection in networks
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
• A new look at the statistical model identification

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