On the public citation graph
On some significance tests in cluster analysis
0 signed · 150 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cite.330470215openalex-snapshot
Automatkche Klassifikation. Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten. (Cluster‐Analyse). Von H. H. Bock. Vandenhoeck & Ruprecht, Gottingen‐Zürich 1974. 1. Aufl., 480 S., 54 Abb., Ln. DM 82,–
inferredcites →https://doi.org/10.1007/978-1-4614-1412-4_60openalex-snapshot
Ordered Families of Distributions
inferredcites →https://doi.org/10.1007/978-3-642-69024-2_26openalex-snapshot
Significance Tests for Clusters: Overview and Comments
inferredcites →https://doi.org/10.1007/bf00535033openalex-snapshot
Die asymptotische Verteilung von Koinzidenzen
inferredcites →https://doi.org/10.1007/bf01029298openalex-snapshot
The sampling distribution of theW statistic of disjunction for the arbitrary division of a random rectangular distribution
inferredcites →https://doi.org/10.1007/bf02312508openalex-snapshot
A method for testing the distinctness of clusters: A test of the disjunction of two clusters in Euclidean space as measured by their overlap
inferredcites →https://doi.org/10.1007/bf02614243openalex-snapshot
Statistische Modelle und Bayessche Verfahren zur Bestimmung einer unbekannten Klassifikation normalverteilter zufälliger Vektoren
inferredcites →https://doi.org/10.1016/0031-3203(79)90034-7openalex-snapshot
Validity studies in clustering methodologies
inferredcites →https://doi.org/10.1016/0047-259x(75)90040-8openalex-snapshot
On the unimodality of spherically symmetric stable distribution functions
inferredcites →https://doi.org/10.1016/0098-3004(79)90001-3openalex-snapshot
Basic program for a significance test for two clusters in euclidean space as measured by their overlap
inferredcites →https://doi.org/10.1016/b978-0-12-714250-0.50007-3openalex-snapshot
Distribution Problems in Clustering
inferredcites →https://doi.org/10.1017/s0001867800042932openalex-snapshot
Limit theorems for dissociated random variables
inferredcites →https://doi.org/10.1080/01621459.1969.10501084openalex-snapshot
Percentage Points of a Test for Clusters
inferredcites →https://doi.org/10.1080/01621459.1974.10480190openalex-snapshot
Probabilities for the Size of Largest Clusters and Smallest Intervals
inferredcites →https://doi.org/10.1080/01621459.1979.10481675openalex-snapshot
Multivariate Tests for Clusters
inferredcites →https://doi.org/10.1093/biomet/65.2.273openalex-snapshot
Asymptotic Behaviour of Classification Maximum Likelihood Estimates
inferredcites →https://doi.org/10.1109/tit.1982.1056471openalex-snapshot
Multiple local optima in vector quantizers (Corresp.)
inferredcites →https://doi.org/10.1109/tit.1982.1056480openalex-snapshot
Sufficient conditions for uniqueness of a locally optimal quantizer for a class of convex error weighting functions
inferredcites →https://doi.org/10.1109/tit.1982.1056481openalex-snapshot
Quantization and the method ofk-means
inferredcites →https://doi.org/10.1109/tit.1983.1056622openalex-snapshot
Uniqueness of locally optimal quantizer for log-concave density and convex error weighting function
inferredcites →https://doi.org/10.1137/0108017openalex-snapshot
A Test of Fit Based on the Largest Sample Spacing
inferredcites →https://doi.org/10.1177/19.7.426openalex-snapshot
SUBPOPULATIONS OF BLOOD LYMPHOCYTES DEMONSTRATED BY QUANTITATIVE CYTOCHEMISTRY
inferredcites →https://doi.org/10.1207/s15327906mbr0503_6openalex-snapshot
PATTERN CLUSTERING BY MULTIVARIATE MIXTURE ANALYSIS
inferredcites →https://doi.org/10.1214/aop/1176993668openalex-snapshot
Sums of Functions of Nearest Neighbor Distances, Moment Bounds, Limit Theorems and a Goodness of Fit Test
inferredcites →https://doi.org/10.1214/aop/1176993713openalex-snapshot
A Central Limit Theorem for $k$-Means Clustering
inferredcites →https://doi.org/10.1214/aop/1176997037openalex-snapshot
Probabilities for a $k$th Nearest Neighbor Problem on the Line
inferredcites →https://doi.org/10.1214/aos/1176344071openalex-snapshot
Asymptotic Distributions for Clustering Criteria
inferredcites →https://doi.org/10.1214/aos/1176344789openalex-snapshot
On the Asymptotic Distribution of $k$-Spacings with Applications to Goodness-of-Fit Tests
inferredcites →https://doi.org/10.1214/aos/1176345339openalex-snapshot
Strong Consistency of $K$-Means Clustering
inferredcites →https://doi.org/10.1214/aos/1176346051openalex-snapshot
Goodness of Fit Testing in $\mathbb{R}^m$ Based on the Weighted Empirical Distribution of Certain Nearest Neighbor Statistics
inferredcites →https://doi.org/10.1214/aos/1176346052openalex-snapshot
An Infinite-Dimensional Approximation for Nearest Neighbor Goodness of Fit Tests
inferredcites →https://doi.org/10.18910/5329openalex-snapshot
Contributions to the theory of systematic statistics. I
inferredcites →https://doi.org/10.2307/1402912openalex-snapshot
Automatische Klassifikation. Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten (Cluster-Analyse)
inferredcites →https://doi.org/10.2307/2987867openalex-snapshot
A Familiar Statistic in an Unfamiliar Guise-A Problem in Clustering
inferredcites →https://doi.org/10.2307/3213486openalex-snapshot
The limit distribution for maxima of ‘weighted' rth-nearest-neighbour distances
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/jsfa.3215openalex-snapshot
Unsupervised classification methods in food sciences: discussion and outlook
inferred← citeshttps://doi.org/10.1007/11893028_96openalex-snapshot
A Competitive Co-evolving Support Vector Clustering
inferred← citeshttps://doi.org/10.1007/3-540-28397-8_19openalex-snapshot
The Number of Clusters in Market Segmentation
inferred← citeshttps://doi.org/10.1007/978-0-387-69319-4_19openalex-snapshot
Clustering Proteomics Data Using Bayesian Principal Component Analysis
inferred← citeshttps://doi.org/10.1007/978-3-540-45219-5_13openalex-snapshot
Validation Techniques in Text Mining (with Application to the Processing of Open-ended Questions)
inferred← citeshttps://doi.org/10.1007/978-3-540-68860-0_14openalex-snapshot
VCV2 – Visual Cluster Validity
inferred← citeshttps://doi.org/10.1007/978-3-540-73560-1_15openalex-snapshot
Clustering Methods: A History of k-Means Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-642-33757-4_20openalex-snapshot
Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
inferred← citeshttps://doi.org/10.1007/978-3-642-48417-9_96openalex-snapshot
Improvement of Stability in Cluster Analysis and Principal Components Analysis by Special Weighting the Variables
inferred← citeshttps://doi.org/10.1007/978-3-642-50974-2_3openalex-snapshot
A k n -Nearest Neighbour Algorithm for Unimodal Clusters
inferred← citeshttps://doi.org/10.1007/978-3-642-51175-2_1openalex-snapshot
Classification and Clustering: Problems for the Future
inferred← citeshttps://doi.org/10.1007/978-3-642-51175-2_20openalex-snapshot
An examination of procedures for determining the number of clusters in a data set
inferred← citeshttps://doi.org/10.1007/978-3-642-51175-2_21openalex-snapshot
The gap test: an optimal method for determining the number of natural classes in cluster analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-57280-7_2openalex-snapshot
Regression-Type Models for Kohonen’s Self-Organizing Networks
inferred← citeshttps://doi.org/10.1007/978-3-642-57991-2_29openalex-snapshot
Clustering Algorithms and Cluster Validation
inferred← citeshttps://doi.org/10.1007/978-3-642-58250-9_33openalex-snapshot
Clusters of Bibliographic References: A Brief Case Study
inferred← citeshttps://doi.org/10.1007/978-3-642-60187-3_4openalex-snapshot
Clustering and Neural Network Approaches
inferred← citeshttps://doi.org/10.1007/978-3-642-72253-0_37openalex-snapshot
Clustering and Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-642-75040-3_2openalex-snapshot
Probabilistic Aspects in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-76307-6_7openalex-snapshot
Multigraphs for the Uncovering and Testing of Structures
inferred← citeshttps://doi.org/10.1007/978-3-642-79999-0_22openalex-snapshot
On the Longest Edge of the Minimal Spanning Tree
inferred← citeshttps://doi.org/10.1007/978-3-642-79999-0_3openalex-snapshot
Null Models in Cluster Validation
inferred← citeshttps://doi.org/10.1007/978-3-642-80098-6_7openalex-snapshot
Probability Distributions on Indexed Dendrograms and Related Problems of Classifiability
inferred← citeshttps://doi.org/10.1007/978-3-662-12433-8_4openalex-snapshot
Clustering and Self-Organizing Networks: Regression-Type Models and Optimization Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-7091-2668-4_5openalex-snapshot
Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps
inferred← citeshttps://doi.org/10.1007/978-4-431-65950-1_1openalex-snapshot
Probabilistic Aspects in Classification
inferred← citeshttps://doi.org/10.1007/978-4-431-65950-1_6openalex-snapshot
How to validate phylogenetic trees? A stepwise procedure
inferred← citeshttps://doi.org/10.1007/978-94-011-0800-3_4openalex-snapshot
Information and Entropy in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-94-011-3418-7_14openalex-snapshot
Knowing When to Stop: Cluster Concept — Concept Cluster
inferred← citeshttps://doi.org/10.1007/bf00436033openalex-snapshot
Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups
inferred← citeshttps://doi.org/10.1007/bf01246098openalex-snapshot
An entropy criterion for assessing the number of clusters in a mixture model
inferred← citeshttps://doi.org/10.1007/bf02616246openalex-snapshot
Large-sample results for optimization-based clustering methods
inferred← citeshttps://doi.org/10.1007/s00357-003-0005-5openalex-snapshot
Model-based Clustering with Noise: Bayesian Inference and Estimation
inferred← citeshttps://doi.org/10.1007/s00357-010-9069-1openalex-snapshot
MCS: A Method for Finding the Number of Clusters
inferred← citeshttps://doi.org/10.1007/s005000050019openalex-snapshot
A geometric approach to cluster validity for normal mixtures
inferred← citeshttps://doi.org/10.1007/s10032-009-0089-5openalex-snapshot
An effective coherence measure to determine topical consistency in user-generated content
inferred← citeshttps://doi.org/10.1007/s10257-015-0299-yopenalex-snapshot
Understanding Internet service switching behaviour based on the stage model
inferred← citeshttps://doi.org/10.1007/s10722-009-9448-9openalex-snapshot
Structure of wild annual sunflower (Helianthus annuus L.) accessions based on agro-morphological traits
inferred← citeshttps://doi.org/10.1007/s10851-006-9176-0openalex-snapshot
A Unified Framework for Detecting Groups and Application to Shape Recognition
inferred← citeshttps://doi.org/10.1007/s10928-005-0040-4openalex-snapshot
Cluster Analysis: An Alternative Method for Covariate Selection in population Pharmacokinetic Modeling
inferred← citeshttps://doi.org/10.1007/s11030-006-9023-7openalex-snapshot
V-cluster algorithm: A new algorithm for clustering molecules based upon numeric data
inferred← citeshttps://doi.org/10.1007/s11634-009-0044-9openalex-snapshot
Trimming algorithms for clustering contaminated grouped data and their robustness
inferred← citeshttps://doi.org/10.1007/s11634-009-0047-6openalex-snapshot
Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables
inferred← citeshttps://doi.org/10.1007/s11634-016-0278-2openalex-snapshot
Probabilistic clustering via Pareto solutions and significance tests
inferred← citeshttps://doi.org/10.1016/0031-3203(87)90034-3openalex-snapshot
How many clusters are best? - An experiment
inferred← citeshttps://doi.org/10.1016/0031-3203(87)90081-1openalex-snapshot
Bootstrap technique in cluster analysis
inferred← citeshttps://doi.org/10.1016/0140-9883(94)90035-3openalex-snapshot
Lifecycle analysis of gasoline expenditure patterns
inferred← citeshttps://doi.org/10.1016/0167-7152(90)90018-3openalex-snapshot
Some peculiar boundary phenomena for extremes of rth nearest neighbor links
inferred← citeshttps://doi.org/10.1016/0167-9473(94)90085-xopenalex-snapshot
Identifying genuine clusters in a classification
inferred← citeshttps://doi.org/10.1016/0167-9473(96)88919-5openalex-snapshot
Probabilistic models in cluster analysis
inferred← citeshttps://doi.org/10.1016/b978-0-12-809633-8.20385-3openalex-snapshot
Computation Cluster Validation in the Big Data Era
inferred← citeshttps://doi.org/10.1016/j.apenergy.2021.116971openalex-snapshot
A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables
inferred← citeshttps://doi.org/10.1016/j.cie.2011.04.016openalex-snapshot
Product structure (BOM)-based product similarity measures using orthogonal procrustes approach
inferred← citeshttps://doi.org/10.1016/j.csda.2004.10.012openalex-snapshot
Loevinger's measures of rule quality for assessing cluster stability
inferred← citeshttps://doi.org/10.1016/j.csda.2009.08.023openalex-snapshot
Using combinatorial optimization in model-based trimmed clustering with cardinality constraints
inferred← citeshttps://doi.org/10.1016/j.eswa.2019.01.074openalex-snapshot
Estimating the number of clusters in a dataset via consensus clustering
inferred← citeshttps://doi.org/10.1016/j.foreco.2012.01.024openalex-snapshot
Developing custom fire behavior fuel models from ecologically complex fuel structures for upper Atlantic Coastal Plain forests
inferred← citeshttps://doi.org/10.1016/j.inffus.2017.04.008openalex-snapshot
Collaborative clustering: Why, when, what and how
inferred← citeshttps://doi.org/10.1016/j.infsof.2006.09.002openalex-snapshot
Identification of factors that influence defect injection and detection in development of software intensive products
inferred← citeshttps://doi.org/10.1016/j.tcs.2012.01.024openalex-snapshot
Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis
inferred← citeshttps://doi.org/10.1016/s0167-5060(08)70379-0openalex-snapshot
Probability Models for Random Multigraphs with Applications in Cluster Analysis
inferred← citeshttps://doi.org/10.1016/s0167-9473(96)00022-9openalex-snapshot
On the number of clusters
inferred← citeshttps://doi.org/10.1017/s0025315408002403openalex-snapshot
Ontogenetic niche feeding partitioning in juvenile of white sea catfish Genidens barbus in estuarine environments, southern Brazil
inferred← citeshttps://doi.org/10.1023/a:1009562314684openalex-snapshot
Cluster Analysis of a Mixture of Multivariate Probability Distributions under Uniform ‘Contamination’
inferred← citeshttps://doi.org/10.1023/a:1023949509487openalex-snapshot
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
inferred← citeshttps://doi.org/10.1037/0012-1649.38.3.446openalex-snapshot
Heterogeneity among peer-rejected boys across middle childhood: Developmental pathways of social behavior.
inferred← citeshttps://doi.org/10.1051/ro/1986200302291openalex-snapshot
Étude exhaustive de l'algorithme de réallocation-recentrage dans un cas simple
inferred← citeshttps://doi.org/10.1080/00401706.1990.10484648openalex-snapshot
Algorithms for Clustering Data
inferred← citeshttps://doi.org/10.1080/00401706.1992.10485257openalex-snapshot
Finding Groups in Data: An Introduction to Chster Analysis
inferred← citeshttps://doi.org/10.1080/01621459.1992.10476277openalex-snapshot
Information Ratios for Validating Mixture Analyses
inferred← citeshttps://doi.org/10.1080/02331939408843931openalex-snapshot
An asymptotic result on principal points for univariate distributions
inferred← citeshttps://doi.org/10.1080/08839510701526541openalex-snapshot
A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING
inferred← citeshttps://doi.org/10.1080/13682820600806680openalex-snapshot
Subtyping of children with developmental dyslexia via bootstrap aggregated clustering and the gap statistic: comparison with the double‐deficit hypothesis
inferred← citeshttps://doi.org/10.1080/17520840902726268openalex-snapshot
Strategic groups and heterogeneous technologies: an application to the US banking industry
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa087openalex-snapshot
Statistical significance of cluster membership for unsupervised evaluation of cell identities
inferred← citeshttps://doi.org/10.1093/bioinformatics/btg119openalex-snapshot
Fuzzy C-means method for clustering microarray data
inferred← citeshttps://doi.org/10.1093/bioinformatics/bti383openalex-snapshot
A novel approach for clustering proteomics data using Bayesian fast Fourier transform
inferred← citeshttps://doi.org/10.1109/hicss.2010.77openalex-snapshot
Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
inferred← citeshttps://doi.org/10.1109/icmla.2012.146openalex-snapshot
A Hybrid Method for Estimating the Predominant Number of Clusters in a Data Set
inferred← citeshttps://doi.org/10.1109/icmlc.2006.258591openalex-snapshot
A New Approach to Hierarchical Clustering Using Partial Least Squares
inferred← citeshttps://doi.org/10.1109/robot.2007.363761openalex-snapshot
Space-time A Contrario Clustering for Detecting Coherent Motions
inferred← citeshttps://doi.org/10.1109/tsmcb.2012.2205679openalex-snapshot
A Cluster Validity Framework Based on Induced Partition Dissimilarity
inferred← citeshttps://doi.org/10.1146/annurev.es.17.110186.002231openalex-snapshot
PHENETIC TAXONOMY: Theory and Methods
inferred← citeshttps://doi.org/10.1146/annurev.ps.43.020192.001125openalex-snapshot
Combinatorial Data Analysis
inferred← citeshttps://doi.org/10.11606/t.12.2009.tde-10092009-111233openalex-snapshot
Contribuições ao estudo dos estilos de vida: comportamento de compra e uso de crédito
inferred← citeshttps://doi.org/10.1162/089976601753196030openalex-snapshot
Resampling Method for Unsupervised Estimation of Cluster Validity
inferred← citeshttps://doi.org/10.1175/2009jhm1104.1openalex-snapshot
Influence of Daily Rainfall Characteristics on Regional Summertime Precipitation over the Southwestern United States
inferred← citeshttps://doi.org/10.1177/014662168701100401openalex-snapshot
Methodology Review: Clustering Methods
inferred← citeshttps://doi.org/10.1186/gb-2002-3-7-research0036openalex-snapshot
A prediction-based resampling method for estimating the number of clusters in a dataset
inferred← citeshttps://doi.org/10.1198/016214508000000454openalex-snapshot
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
inferred← citeshttps://doi.org/10.1348/000711005x48266openalex-snapshot
K‐means clustering: A half‐century synthesis
inferred← citeshttps://doi.org/10.14254/2071-8330.2017/10-3/17openalex-snapshot
Segmentation of Portuguese customers’ expectations from fitness programs
inferred← citeshttps://doi.org/10.14430/arctic175openalex-snapshot
Effects of Climate Change on the Seasonality of Weather for Tourism in Alaska
inferred← citeshttps://doi.org/10.1523/jneurosci.0980-08.2009openalex-snapshot
Development of Spontaneous Recurrent Seizures after Kainate-Induced Status Epilepticus
inferred← citeshttps://doi.org/10.22004/ag.econ.157585openalex-snapshot
Hierarchical Cluster Analysis – Various Approaches to Data Preparation
inferred← citeshttps://doi.org/10.2307/2981629openalex-snapshot
A Review of Hierarchical Classification
inferred← citeshttps://doi.org/10.31274/etd-180810-803openalex-snapshot
Computational geological approaches for assessing the diversity and ecological distribution of fossils
inferred← citeshttps://doi.org/10.31274/rtd-180813-13825openalex-snapshot
Statistical analysis using finite mixtures of normal linear models
inferred← citeshttps://doi.org/10.31274/rtd-180813-9487openalex-snapshot
A cluster analytic approach to interest measurement with the ACT Interest Inventory
inferred← citeshttps://doi.org/10.3233/ida-130596openalex-snapshot
Estimating the predominant number of clusters in a dataset
inferred← citeshttps://doi.org/10.3233/ida-2009-0390openalex-snapshot
Quality indices for (practical) clustering evaluation
inferred← citeshttps://doi.org/10.48550/arxiv.1102.2915openalex-snapshot
Algorithms for Internal Validation Clustering Measures in the Post Genomic Era
inferred← citeshttps://doi.org/10.48550/arxiv.1910.02566openalex-snapshot
Gaussian Mixture Clustering Using Relative Tests of Fit
inferred← citeshttps://doi.org/10.5604/01.3001.0010.4853openalex-snapshot
TESTING FOR REVEALING OF DATA STRUCTURE BASED ON THE HYBRID APPROACH
inferred← citeshttps://doi.org/10.59139/ps.2011.03-04.8openalex-snapshot
Ocena jakości wyników grupowania – przegląd bibliografii
inferred← citeshttps://doi.org/10.6100/ir631481openalex-snapshot
Effects of virtual product development on product quality and their influencing factors
inferred← citeshttps://doi.org/10.71781/629openalex-snapshot
Individual and social determinants of multiple chronic disease behavioural risk factors in Canadian children and adolescents
inferred← citeshttps://doi.org/10.7916/d88w3mgbopenalex-snapshot
Good Mathematics Teaching: Perspectives of Beginning Secondary Teachers
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.