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An Algorithm for Generating Artificial Test Clusters
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf02293899openalex-snapshot
A Monte Carlo Study of Thirty Internal Criterion Measures for Cluster Analysis
inferredcites →https://doi.org/10.1007/bf02293907openalex-snapshot
An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms
inferredcites →https://doi.org/10.1007/bf02294245openalex-snapshot
An Examination of Procedures for Determining the Number of Clusters in a Data Set
inferredcites →https://doi.org/10.1016/0031-3203(79)90034-7openalex-snapshot
Validity studies in clustering methodologies
inferredcites →https://doi.org/10.1016/0031-3203(80)90001-1openalex-snapshot
The validation of four ultrametric clustering algorithms
inferredcites →https://doi.org/10.1016/0031-3203(80)90002-3openalex-snapshot
Monte Carlo comparisons of selected clustering procedures
inferredcites →https://doi.org/10.1037//0033-2909.83.3.377openalex-snapshot
Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods.
inferredcites →https://doi.org/10.1093/comjnl/20.4.359openalex-snapshot
Hierarchical grouping methods and stopping rules: an evaluation
inferredcites →https://doi.org/10.1093/ej/33.130.269openalex-snapshot
Journal of the Royal Statistical Society.
inferredcites →https://doi.org/10.1109/tpami.1983.4767342openalex-snapshot
The Effect of Cluster Size, Dimensionality, and the Number of Clusters on Recovery of True Cluster Structure
inferredcites →https://doi.org/10.1111/j.1540-5915.1980.tb01168.xopenalex-snapshot
A NOTE ON PROCEDURES FOR TESTING THE QUALITY OF A CLUSTERING OF A SET OF OBJECTS
inferredcites →https://doi.org/10.1177/0013164484441003openalex-snapshot
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inferredcites →https://doi.org/10.1177/014662168000400107openalex-snapshot
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inferredcites →https://doi.org/10.1207/s15327906mbr1403_6openalex-snapshot
Mixture Model Tests Of Hierarchical Clustering Algorithms: The Problem Of Classifying Everybody
inferredcites →https://doi.org/10.1207/s15327906mbr1603_7openalex-snapshot
A Review Of Monte Carlo Tests Of Cluster Analysis
inferredcites →https://doi.org/10.1207/s15327906mbr2001_6openalex-snapshot
Asymptotic and Finite Sample Characteristics of Four External Criterion Measures
inferredcites →https://doi.org/10.2307/2529565openalex-snapshot
391: A Monte Carlo Comparison of Six Clustering Procedures
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1994.tb01609.xopenalex-snapshot
VARIABLE SCREENING FOR CLUSTER ANALYSIS
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1994.tb01619.xopenalex-snapshot
A PRELIMINARY STUDY OF THE EFFECTS OF WITHIN‐GROUP COVARIANCE STRUCTURE ON RECOVERY IN CLUSTER ANALYSIS
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1999.tb01806.xopenalex-snapshot
THE EFFECTS OF WITHIN‐GROUP COVARIANCE STRUCTURE ON RECOVERY IN CLUSTER ANALYSIS: II. EXTENSION TO THE P‐DIMENSIONAL CASE
inferred← citeshttps://doi.org/10.1002/widm.1330openalex-snapshot
Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey
inferred← citeshttps://doi.org/10.1002/widm.1511openalex-snapshot
A white paper on good research practices in benchmarking: The case of cluster analysis
inferred← citeshttps://doi.org/10.1007/11875581_97openalex-snapshot
A Non-parametric Method for Data Clustering with Optimal Variable Weighting
inferred← citeshttps://doi.org/10.1007/3-540-46502-2_13openalex-snapshot
A Data-Clustering Algorithm on Distributed Memory Multiprocessors
inferred← citeshttps://doi.org/10.1007/978-3-031-70368-3_1openalex-snapshot
Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-031-96033-8_85openalex-snapshot
Novel Data Generator for Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-13159-7_80openalex-snapshot
Replicated Weights Determination in the Synclus Optimal Variable Weights Clustering Procedure
inferred← citeshttps://doi.org/10.1007/978-3-319-17356-6_36openalex-snapshot
An Empirical Assessment of Two Univariate Screening Measures in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-42056-1_13openalex-snapshot
Applying the Gradient Projection Method to a Model of Proportional Membership for Fuzzy Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-04274-4_51openalex-snapshot
A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means
inferred← citeshttps://doi.org/10.1007/978-3-642-10745-0_19openalex-snapshot
Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures
inferred← citeshttps://doi.org/10.1007/978-3-642-17103-1_6openalex-snapshot
Standardizing Variables in K-means Clustering
inferred← citeshttps://doi.org/10.1007/978-3-642-23878-9_38openalex-snapshot
A Novel Multi-Objective Genetic Algorithm for Clustering
inferred← citeshttps://doi.org/10.1007/978-3-642-24466-7_7openalex-snapshot
Effect of Data Standardization on the Result of k-Means Clustering
inferred← citeshttps://doi.org/10.1007/978-3-642-32451-2_5openalex-snapshot
Point Symmetry-Based Distance Measures and Their Applications to Clustering
inferred← citeshttps://doi.org/10.1007/978-3-642-73489-2_27openalex-snapshot
The Effect of Measurement Error on Determining the Number of Clusters in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/bf01237937openalex-snapshot
A Monte Carlo evaluation of the moving method, k-means and two self-organising neural networks
inferred← citeshttps://doi.org/10.1007/bf01246105openalex-snapshot
Measuring the influence of individual data points in a cluster analysis
inferred← citeshttps://doi.org/10.1007/bf01897163openalex-snapshot
A study of standardization of variables in cluster analysis
inferred← citeshttps://doi.org/10.1007/bf01908588openalex-snapshot
A validation study of a variable weighting algorithm for cluster analysis
inferred← citeshttps://doi.org/10.1007/bf02294390openalex-snapshot
A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-means Clustering
inferred← citeshttps://doi.org/10.1007/bf02294838openalex-snapshot
A Variable-Selection Heuristic for K-means Clustering
inferred← citeshttps://doi.org/10.1007/bf02295433openalex-snapshot
A Comparison of the Classification Capabilities of the 1-Dimensional Kohonen Neural Network with Two Pratitioning and Three Hierarchical Cluster Analysis Algorithms
inferred← citeshttps://doi.org/10.1007/s00357-001-0018-xopenalex-snapshot
Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software
inferred← citeshttps://doi.org/10.1007/s00357-005-0015-6openalex-snapshot
OCLUS: An Analytic Method for Generating Clusters with Known Overlap
inferred← citeshttps://doi.org/10.1007/s00357-006-0018-yopenalex-snapshot
Generation of Random Clusters with Specified Degree of Separation
inferred← citeshttps://doi.org/10.1007/s00357-007-0006-xopenalex-snapshot
Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches
inferred← citeshttps://doi.org/10.1007/s00357-015-9178-yopenalex-snapshot
On the Added Value of Bootstrap Analysis for K-Means Clustering
inferred← citeshttps://doi.org/10.1007/s00357-019-09356-yopenalex-snapshot
Editorial: Journal of Classification Vol. 36-3
inferred← citeshttps://doi.org/10.1007/s10489-010-0220-9openalex-snapshot
On the equivalence between nonnegative tensor factorization and tensorial probabilistic latent semantic analysis
inferred← citeshttps://doi.org/10.1007/s10921-013-0177-9openalex-snapshot
Quantification of the Uncertainty of Pattern Recognition Approaches Applied to Acoustic Emission Signals
inferred← citeshttps://doi.org/10.1007/s11227-006-0002-7openalex-snapshot
Parallel bisecting k-means with prediction clustering algorithm
inferred← citeshttps://doi.org/10.1007/s11336-001-0926-zopenalex-snapshot
Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units
inferred← citeshttps://doi.org/10.1007/s11336-003-1272-1openalex-snapshot
Validating Clusters with the Lower Bound for Sum-of-Squares Error
inferred← citeshttps://doi.org/10.1007/s11336-007-9013-4openalex-snapshot
A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning
inferred← citeshttps://doi.org/10.1007/s11336-007-9019-yopenalex-snapshot
Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures
inferred← citeshttps://doi.org/10.1007/s11336-017-9561-1openalex-snapshot
Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data
inferred← citeshttps://doi.org/10.1007/s11412-012-9160-1openalex-snapshot
Participatory learning through behavioral and cognitive engagements in an online collective information searching activity
inferred← citeshttps://doi.org/10.1007/s11634-024-00592-8openalex-snapshot
Comparison of internal evaluation criteria in hierarchical clustering of categorical data
inferred← citeshttps://doi.org/10.1007/s41237-018-0068-6openalex-snapshot
Clustering and dimension reduction for mixed variables
inferred← citeshttps://doi.org/10.1016/0003-2670(93)80130-dopenalex-snapshot
On k-medoid clustering of large data sets with the aid of a genetic algorithm: background, feasiblity and comparison
inferred← citeshttps://doi.org/10.1016/0305-0483(95)00011-copenalex-snapshot
The comparative ability of self-organizing neural networks to define cluster structure
inferred← citeshttps://doi.org/10.1016/0377-2217(96)00038-0openalex-snapshot
A comparison of SOM neural network and hierarchical clustering methods
inferred← citeshttps://doi.org/10.1016/0377-2217(96)00046-xopenalex-snapshot
Comparative performance of the FSCL neural net and K-means algorithm for market segmentation
inferred← citeshttps://doi.org/10.1016/j.buildenv.2022.109620openalex-snapshot
Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis
inferred← citeshttps://doi.org/10.1016/j.camwa.2005.05.009openalex-snapshot
Application of ant K-means on clustering analysis
inferred← citeshttps://doi.org/10.1016/j.chb.2011.01.005openalex-snapshot
Applying social bookmarking to collective information searching (CIS): An analysis of behavioral pattern and peer interaction for co-exploring quality online resources
inferred← citeshttps://doi.org/10.1016/j.cor.2006.07.001openalex-snapshot
Scale-invariant clustering with minimum volume ellipsoids
inferred← citeshttps://doi.org/10.1016/j.dss.2004.04.010openalex-snapshot
Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce
inferred← citeshttps://doi.org/10.1016/j.ejor.2005.03.039openalex-snapshot
Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110836openalex-snapshot
Generating multidimensional clusters with support lines
inferred← citeshttps://doi.org/10.1016/j.patcog.2005.10.027openalex-snapshot
A partitional clustering algorithm validated by a clustering tendency index based on graph theory
inferred← citeshttps://doi.org/10.1016/j.patrec.2011.09.018openalex-snapshot
Pattern recognition approach to identify natural clusters of acoustic emission signals
inferred← citeshttps://doi.org/10.1016/s0167-8116(02)00080-0openalex-snapshot
Retail segmentation using artificial neural networks
inferred← citeshttps://doi.org/10.1016/s0305-0548(01)00043-0openalex-snapshot
Integration of self-organizing feature map and K-means algorithm for market segmentation
inferred← citeshttps://doi.org/10.1016/s0360-8352(02)00048-7openalex-snapshot
Cluster analysis in industrial market segmentation through artificial neural network
inferred← citeshttps://doi.org/10.1016/s0898-1221(99)00090-5openalex-snapshot
A genetic algorithm approach to cluster analysis
inferred← citeshttps://doi.org/10.1016/s1441-3582(99)70212-0openalex-snapshot
A Preliminary Study of Influential Observation Measurement in K -Means Clustering Procedure
inferred← citeshttps://doi.org/10.1023/a:1009769707641openalex-snapshot
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
inferred← citeshttps://doi.org/10.1023/a:1020321132568openalex-snapshot
Evaluating the Appropriateness of Market Segmentation Solutions Using Artificial Neural Networks and the Membership Clustering Criterion
inferred← citeshttps://doi.org/10.1037/1082-989x.11.2.178openalex-snapshot
Profiling local optima in K-means clustering: Developing a diagnostic technique.
inferred← citeshttps://doi.org/10.1037/1082-989x.8.3.294openalex-snapshot
Local Optima in K-Means Clustering: What You Don't Know May Hurt You.
inferred← citeshttps://doi.org/10.1037/1082-989x.9.4.510openalex-snapshot
Clustering Binary Data in the Presence of Masking Variables.
inferred← citeshttps://doi.org/10.1037/a0023346openalex-snapshot
Choosing the number of clusters in Κ-means clustering.
inferred← citeshttps://doi.org/10.1038/s41598-021-83340-8openalex-snapshot
Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark
inferred← citeshttps://doi.org/10.1080/00273170701836695openalex-snapshot
A New Variable Weighting and Selection Procedure for K-means Cluster Analysis
inferred← citeshttps://doi.org/10.1080/00273171.2012.673952openalex-snapshot
Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction
inferred← citeshttps://doi.org/10.1080/03610918.2017.1367805openalex-snapshot
Clustering of seasonal events: A simulation study using circular methods
inferred← citeshttps://doi.org/10.1080/10170660209509214openalex-snapshot
INTEGRATION OF ADAPTIVE RESONANCE THEORY II NEURAL NETWORK AND GENETIC K-MEANS ALGORITHM FOR DATA MINING
inferred← citeshttps://doi.org/10.1081/sac-120017869openalex-snapshot
Further Results on Lund's Statistic for Identifying Cluster in a Circular Data Set with Application to Time Series
inferred← citeshttps://doi.org/10.1081/sta-120037266openalex-snapshot
A New Similarity Measure and Its Use in Determining the Number of Clusters in a Multivariate Data Set
inferred← citeshttps://doi.org/10.1089/cmb.2009.0129openalex-snapshot
Biological Cluster Evaluation for Gene Function Prediction
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa243openalex-snapshot
Pooled variable scaling for cluster analysis
inferred← citeshttps://doi.org/10.1108/978-1-64113-563-420251011openalex-snapshot
A Systematic Investigation of Within-Subject and between-Subject Covariance Structures in Growth Mixture Models
inferred← citeshttps://doi.org/10.1109/access.2019.2917004openalex-snapshot
A Meta-Learning Recommendation System for Characterizing Unsupervised Problems: On Using Quality Indices to Describe Data Conformations
inferred← citeshttps://doi.org/10.1109/access.2019.2953528openalex-snapshot
A Design of Experiments Comparative Study on Clustering Methods
inferred← citeshttps://doi.org/10.1109/cimca.2005.1631265openalex-snapshot
A Comparison Study of Cluster Validity Indices Using a Nonhierarchical Clustering Algorithm
inferred← citeshttps://doi.org/10.1109/ieem.2009.5373445openalex-snapshot
Developing a novel patent map to explore R&D directions and technical gaps for thin-film photovoltaic industry
inferred← citeshttps://doi.org/10.1109/isspa.2003.1224814openalex-snapshot
Stability-based cluster analysis applied to microarray data
inferred← citeshttps://doi.org/10.1109/sws.2009.5271791openalex-snapshot
PGMCLU: A novel parallel grid-based clustering algorithm for multi-density datasets
inferred← citeshttps://doi.org/10.1109/tfuzz.2003.809889openalex-snapshot
Modeling proportional membership in fuzzy clustering
inferred← citeshttps://doi.org/10.1109/ukci.2013.6651311openalex-snapshot
Experimental evaluation of cluster quality measures
inferred← citeshttps://doi.org/10.1111/j.1540-5915.1991.tb01910.xopenalex-snapshot
A Computational Study of Replicated Clustering with an Application to Market Segmentation*
inferred← citeshttps://doi.org/10.1117/12.2194432openalex-snapshot
Spatially constrained clustering over GIS generated suitability maps
inferred← citeshttps://doi.org/10.1145/1458082.1458365openalex-snapshot
Clustering multi-way data via adaptive subspace iteration
inferred← citeshttps://doi.org/10.1145/1571941.1572069openalex-snapshot
Equivalence between nonnegative tensor factorization and tensorial probabilistic latent semantic analysis
inferred← citeshttps://doi.org/10.1145/2479787.2479812openalex-snapshot
Identifying hidden geospatial resources in catalogues
inferred← citeshttps://doi.org/10.1145/3580305.3599258openalex-snapshot
Anomaly Detection with Score Distribution Discrimination
inferred← citeshttps://doi.org/10.1155/s1110865704309078openalex-snapshot
Cluster Structure Inference Based on Clustering Stability with Applications to Microarray Data Analysis
inferred← citeshttps://doi.org/10.11606/d.45.2007.tde-13092007-145328openalex-snapshot
Comparação e escolha de agrupamentos: uma proposta utilizando a entropia
inferred← citeshttps://doi.org/10.1177/002224379903600408openalex-snapshot
HINoV: A New Model to Improve Market Segment Definition by Identifying Noisy Variables
inferred← citeshttps://doi.org/10.1177/014662168701100401openalex-snapshot
Methodology Review: Clustering Methods
inferred← citeshttps://doi.org/10.1177/075910639805800104openalex-snapshot
An Evaluation of Some Clustering Methods for Mixed Mode Variable Data Sets
inferred← citeshttps://doi.org/10.1177/147078539503700302openalex-snapshot
A Comparison of Alternative Approaches to Cluster-Based Market Segmentation
inferred← citeshttps://doi.org/10.1198/jcgs.2009.08054openalex-snapshot
Simulating Data to Study Performance of Finite Mixture Modeling and Clustering Algorithms
inferred← citeshttps://doi.org/10.1201/b15783-4openalex-snapshot
Ensemble Strategies for Neural Network Classifiers
inferred← citeshttps://doi.org/10.1201/b15783-8openalex-snapshot
Ensemble Strategies for Neural Network Classifiers
inferred← citeshttps://doi.org/10.1207/s15327906mb340201openalex-snapshot
A Method for Generating Simulated Plasmodes and Artificial Test Clusters with User-Defined Shape, Size, and Orientation
inferred← citeshttps://doi.org/10.1207/s15327906mbr2703_6openalex-snapshot
A Comparison of Two Approaches to Beta-Flexible Clustering
inferred← citeshttps://doi.org/10.1207/s15327906mbr3002_6openalex-snapshot
The Effects of Within-group Covariance Structure on Recovery in Cluster Analysis: I. The Bivariate Case
inferred← citeshttps://doi.org/10.1207/s15327906mbr3003_5openalex-snapshot
Univariate Screening Measures for Cluster Analysis
inferred← citeshttps://doi.org/10.1214/09-ss053openalex-snapshot
Finite mixture models and model-based clustering
inferred← citeshttps://doi.org/10.12681/eadd/21605openalex-snapshot
Θεωρία ροών επιτυχιών και εφαρμογές
inferred← citeshttps://doi.org/10.1300/j142v04n01_07openalex-snapshot
An Investigation of the Efficacy of Univariate Screening Measures in Defining Market Structure
inferred← citeshttps://doi.org/10.1348/000711005x48266openalex-snapshot
K‐means clustering: A half‐century synthesis
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1014280openalex-snapshot
Fully synthetic replication of complex real biological cell clusters using a novel cluster-based ‘Rosetta-Routine’ computational modelling process
inferred← citeshttps://doi.org/10.15358/0344-1369-2001-2-129openalex-snapshot
Clusteranalyse mittels SOFM
inferred← citeshttps://doi.org/10.18778/7525-695-6openalex-snapshot
Metody selekcji zmiennych w analizie skupień. Nowe procedury
inferred← citeshttps://doi.org/10.21236/ada309141openalex-snapshot
Occupational Analysis and Job Structures.
inferred← citeshttps://doi.org/10.2307/2348731openalex-snapshot
The Effect of Cluster Size, Dimensionality, and Number of Clusters on Recovery of True Cluster Structure Through Chernoff-Type Faces
inferred← citeshttps://doi.org/10.2307/3235592openalex-snapshot
Comparing three classification strategies for use in ecology
inferred← citeshttps://doi.org/10.2316/journal.205.2006.3.205-4159openalex-snapshot
GENETIC CLUSTERING ALGORITHMS: A COMPARISON SIMULATION STUDY
inferred← citeshttps://doi.org/10.3233/ida-2003-7404openalex-snapshot
Algorithms for clustering high dimensional and distributed data
inferred← citeshttps://doi.org/10.3233/ida-2011-0490openalex-snapshot
Tensor clustering via adaptive subspace iteration
inferred← citeshttps://doi.org/10.3233/ida-2012-0545openalex-snapshot
A comparison of clustering quality indices using outliers and noise
inferred← citeshttps://doi.org/10.3390/s20051442openalex-snapshot
Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs
inferred← citeshttps://doi.org/10.35378/gujs.1705566openalex-snapshot
Assessment of Factors Affecting Water Consumption: The Case of İzmir, Türkiye
inferred← citeshttps://doi.org/10.3758/s13428-015-0697-6openalex-snapshot
Examining the effect of initialization strategies on the performance of Gaussian mixture modeling
inferred← citeshttps://doi.org/10.4018/978-1-4666-2473-3.ch004openalex-snapshot
Determination of the Number of Clusters in a Data Set
inferred← citeshttps://doi.org/10.4018/jsds.2011100101openalex-snapshot
Determination of the Number of Clusters in a Data Set
inferred← citeshttps://doi.org/10.48198/njpas/21.a07openalex-snapshot
Bird Species Abundance and Diversity in Montane Forest Research Station, Jos, Plateau State
inferred← citeshttps://doi.org/10.48550/arxiv.2108.01123openalex-snapshot
Metodos de Agrupamentos em dois Estagios
inferred← citeshttps://doi.org/10.5194/esd-15-607-2024openalex-snapshot
Classification of synoptic circulation patterns with a two-stage clustering algorithm using the modified structural similarity index metric (SSIM)
inferred← citeshttps://doi.org/10.5351/csam.2015.22.1.055openalex-snapshot
Variable Selection and Outlier Detection for Automated K-means Clustering
inferred← citeshttps://doi.org/10.5351/kjas.2012.25.3.471openalex-snapshot
A Variable Selection Procedure for K-Means Clustering
inferred← citeshttps://doi.org/10.59170/stattrans-2013-030openalex-snapshot
Empirical evaluation of OCLUS and GenRandomClust algorithms of generating cluster structures
inferred← citeshttps://doi.org/10.7177/sg.2009.v4n2a1openalex-snapshot
Implementing bootstrap in ward´s algorithm to estimate the number of clusters
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