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Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach

https://doi.org/10.1109/tpami.2020.3047489
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Outgoing claims: the papers this work cites, builds on, or argues with.

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On the number of components in a Gaussian mixture model
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A Survey on Feature Weighting Based K-Means Algorithms
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Model-Based Clustering
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Strong consistency of factorial $$K$$ K -means clustering
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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
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Comparison of the predicted and observed secondary structure of T4 phage lysozyme
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Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
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Selection of the number of clusters via the bootstrap method
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Strong consistency of k -parameters clustering
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An optimization algorithm for clustering using weighted dissimilarity measures
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A feature group weighting method for subspace clustering of high-dimensional data
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Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering
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Data clustering: 50 years beyond K-means
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Convex Optimization
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Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization
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Feature Weighting in k-Means Clustering
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Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
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Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
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Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses
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Objective Criteria for the Evaluation of Clustering Methods
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Sparse Convex Clustering
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Consistent selection of the number of clusters via crossvalidation
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How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
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Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
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Clustering disjoint subspaces via sparse representation
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Least squares quantization in PCM
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Toward integrating feature selection algorithms for classification and clustering
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An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
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Survey of Clustering Algorithms
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Maximum Margin Clustering Made Practical
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Twin Support Vector Machine for Clustering
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L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector $k$ -Plane Clustering
inferredcites →https://doi.org/10.1109/tpami.2004.71openalex-snapshot
Simultaneous feature selection and clustering using mixture models
inferredcites →https://doi.org/10.1109/tpami.2005.95openalex-snapshot
Automated variable weighting in k-means type clustering
inferredcites →https://doi.org/10.1109/tpami.2013.57openalex-snapshot
Sparse Subspace Clustering: Algorithm, Theory, and Applications
inferredcites →https://doi.org/10.1109/tpami.2019.2912599openalex-snapshot
On Perfect Clustering of High Dimension, Low Sample Size Data
inferredcites →https://doi.org/10.1111/1467-9868.00293openalex-snapshot
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
inferredcites →https://doi.org/10.1111/biom.12540openalex-snapshot
Convex Biclustering
inferredcites →https://doi.org/10.1111/j.1467-9868.2004.02059.xopenalex-snapshot
Clustering Objects on Subsets of Attributes (with Discussion)
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Variable Selection for Clustering with Gaussian Mixture Models
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Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1111/sjos.12074openalex-snapshot
Strong Consistency of Reduced K‐means Clustering
inferredcites →https://doi.org/10.1126/science.286.5439.531openalex-snapshot
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
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Efficient Maximum Margin Clustering via Cutting Plane Algorithm
inferredcites →https://doi.org/10.1145/3136625openalex-snapshot
Feature Selection
inferredcites →https://doi.org/10.1198/016214506000000113openalex-snapshot
Variable Selection for Model-Based Clustering
inferredcites →https://doi.org/10.1198/jasa.2010.tm09415openalex-snapshot
A Framework for Feature Selection in Clustering
inferredcites →https://doi.org/10.1201/9781584888796.ch10openalex-snapshot
Weighting Method for Feature Selection in K-Means
inferredcites →https://doi.org/10.1214/12-ejs668openalex-snapshot
Regularized k-means clustering of high-dimensional data and its asymptotic consistency
inferredcites →https://doi.org/10.1214/15-aos1423openalex-snapshot
Influential features PCA for high dimensional clustering
inferredcites →https://doi.org/10.1214/16-aos1513openalex-snapshot
Detection and feature selection in sparse mixture models
inferredcites →https://doi.org/10.1214/16-aos1522openalex-snapshot
Phase transitions for high dimensional clustering and related problems
inferredcites →https://doi.org/10.1214/18-ss119openalex-snapshot
Variable selection methods for model-based clustering
inferredcites →https://doi.org/10.1214/aos/1176345339openalex-snapshot
Strong Consistency of $K$-Means Clustering
inferredcites →https://doi.org/10.24963/ijcai.2018/455openalex-snapshot
Robust Multi-view Learning via Half-quadratic Minimization
inferredcites →https://doi.org/10.48550/arxiv.1004.3101openalex-snapshot
Strong Consistency of Prototype Based Clustering in Probabilistic Space
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Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
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Sparse $k$-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional Data Clustering
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Generalized Maximum Margin Clustering and Unsupervised Kernel Learning

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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