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Clustering N Objects into K Groups under Optimal Scaling of Variables

https://doi.org/10.1007/bf02296404
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-0450-1openalex-snapshot
Pattern Recognition with Fuzzy Objective Function Algorithms
inferredcites →https://doi.org/10.1007/978-3-642-51461-6_70openalex-snapshot
Monotone Splines: A Family of Transformations Useful for Data Analysis
inferredcites →https://doi.org/10.1007/bf01908074openalex-snapshot
Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm
inferredcites →https://doi.org/10.1007/bf02293796openalex-snapshot
Quantitative Analysis of Qualitative Data
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/bf02294203openalex-snapshot
Forced Classification: A Simple Application of a Quantification Method
inferredcites →https://doi.org/10.1007/bf02294206openalex-snapshot
Synthesized Clustering: A method for Amalgamating Alternative Clustering Bases with Differential Weighting of Variables
inferredcites →https://doi.org/10.1007/bf02306031openalex-snapshot
Errata for Forced classification: A simple application of a quantification method
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/10.1016/0377-2217(86)90128-1openalex-snapshot
Cluster dissection and analysis
inferredcites →https://doi.org/10.1016/0378-4754(86)90102-3openalex-snapshot
Cluster dissection and analysis.
inferredcites →https://doi.org/10.1080/00401706.1979.10489789openalex-snapshot
The Analysis of Cross-Classified Categorical Data
inferredcites →https://doi.org/10.1080/00401706.1992.10485289openalex-snapshot
Nonlinear Multivariate Analysis
inferredcites →https://doi.org/10.1080/01621459.1958.10501479openalex-snapshot
On Grouping for Maximum Homogeneity
inferredcites →https://doi.org/10.1080/01621459.1967.10500923openalex-snapshot
On Some Invariant Criteria for Grouping Data
inferredcites →https://doi.org/10.1093/biomet/65.1.31openalex-snapshot
Bayesian Cluster Analysis
inferredcites →https://doi.org/10.1111/j.2044-8317.1983.tb00765.xopenalex-snapshot
Non‐linear canonical correlation†
inferredcites →https://doi.org/10.1207/s15327906mbr2003_4openalex-snapshot
Monte Carlo Tests of the Accuracy of Cluster Analysis Algorithms: A Comparison of Hierarchical and Nonhierarchical Methods
inferredcites →https://doi.org/10.1287/mnsc.24.14.1515openalex-snapshot
Integer Programming Solution of a Classification Problem
inferredcites →https://doi.org/10.2307/1270194openalex-snapshot
Compstat: Proceedings in Computational Statistics
inferredcites →https://doi.org/10.2307/1402421openalex-snapshot
The Analysis of Cross-Classified Categorical Data
inferredcites →https://doi.org/10.2307/2286999openalex-snapshot
The Analysis of Cross-Classified Categorical Data.
inferredcites →https://doi.org/10.2307/2290303openalex-snapshot
Nonlinear Multivariate Analysis.
inferredcites →https://doi.org/10.2307/2528823openalex-snapshot
A General Coefficient of Similarity and Some of Its Properties
inferredcites →https://doi.org/10.2307/2529785openalex-snapshot
An Algorithm for Euclidean Sum of Squares Classification
inferredcites →https://doi.org/10.2307/2529904openalex-snapshot
Mathematical Programming in Statistics.
inferredcites →https://doi.org/10.2307/2988136openalex-snapshot
Clustering Algorithms.
inferredcites →https://doi.org/10.4135/9781412984713openalex-snapshot
Latent Class Analysis

What points at this work

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

inferred← citeshttps://doi.org/10.1002/wics.1456openalex-snapshot
Distance‐based clustering of mixed data
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/10.1002/widm.1547openalex-snapshot
Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-00035-0_2openalex-snapshot
Clustering and Prediction of Rankings Within a Kemeny Distance Framework
inferred← citeshttps://doi.org/10.1007/978-3-319-06692-9_18openalex-snapshot
On Joint Dimension Reduction and Clustering of Categorical Data
inferred← citeshttps://doi.org/10.1007/978-3-319-17377-1_23openalex-snapshot
Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches
inferred← citeshttps://doi.org/10.1007/978-3-642-51175-2_24openalex-snapshot
K-means clustering in a low-dimensional Euclidean space
inferred← citeshttps://doi.org/10.1007/978-3-642-59471-7_4openalex-snapshot
MIXISO: a Non-Hierarchical Clustering Method for Mixed-Mode Data
inferred← citeshttps://doi.org/10.1007/978-981-10-0159-8_6openalex-snapshot
Joint Dimension Reduction and Clustering
inferred← citeshttps://doi.org/10.1007/978-981-99-5329-5_9openalex-snapshot
Old and New Perspectives on Optimal Scaling
inferred← citeshttps://doi.org/10.1007/bf02294420openalex-snapshot
Imputation of Missing Categorical Data by Maximizing Internal Consistency
inferred← citeshttps://doi.org/10.1007/s00180-012-0329-xopenalex-snapshot
Iterative factor clustering of binary data
inferred← citeshttps://doi.org/10.1007/s00180-014-0544-8openalex-snapshot
Reduced $$k$$ k -means clustering with MCA in a low-dimensional space
inferred← citeshttps://doi.org/10.1007/s00357-011-9085-9openalex-snapshot
A New Dimension Reduction Method: Factor Discriminant K-means
inferred← citeshttps://doi.org/10.1007/s00357-013-9144-5openalex-snapshot
Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data
inferred← citeshttps://doi.org/10.1007/s00357-016-9218-2openalex-snapshot
Editorial
inferred← citeshttps://doi.org/10.1007/s00357-018-9284-8openalex-snapshot
Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
inferred← citeshttps://doi.org/10.1007/s00357-026-09557-2openalex-snapshot
Composite Likelihood Inference for Simultaneous Clustering and Dimensionality Reduction of Multivariate Mixed-Type Longitudinal Data
inferred← citeshttps://doi.org/10.1007/s10479-025-06745-yopenalex-snapshot
Fuzzy Reduced $$\mathbf {\textit{K}}$$-Means for analyzing high-dimensional social phenomena
inferred← citeshttps://doi.org/10.1007/s11205-018-1972-6openalex-snapshot
Assessing Satisfaction with Public Transport Service by Ordered Multiple Correspondence Analysis
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/s11336-004-1173-xopenalex-snapshot
An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents
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/s11336-016-9514-0openalex-snapshot
Cluster Correspondence Analysis
inferred← citeshttps://doi.org/10.1007/s11336-017-9578-5openalex-snapshot
A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data
inferred← citeshttps://doi.org/10.1007/s41237-018-0068-6openalex-snapshot
Clustering and dimension reduction for mixed variables
inferred← citeshttps://doi.org/10.1007/s41237-018-0069-5openalex-snapshot
A distribution-free soft-clustering method for preference rankings
inferred← citeshttps://doi.org/10.1007/s41237-019-00092-6openalex-snapshot
Special feature: dimension reduction and cluster analysis
inferred← citeshttps://doi.org/10.1007/s42081-018-0022-6openalex-snapshot
A unified representation of simultaneous analysis methods of reduction and clustering
inferred← citeshttps://doi.org/10.1016/b978-0-444-64165-6.01021-1openalex-snapshot
Unsupervised Data Mining: Introduction
inferred← citeshttps://doi.org/10.1016/b978-0-444-81531-6.50024-6openalex-snapshot
Correspondence analysis and classification
inferred← citeshttps://doi.org/10.1016/b978-044452701-1.00063-6openalex-snapshot
Unsupervised Data Mining: Introduction
inferred← citeshttps://doi.org/10.1016/j.aap.2003.09.006openalex-snapshot
Freeway safety as a function of traffic flow
inferred← citeshttps://doi.org/10.1016/j.csda.2008.04.011openalex-snapshot
A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences
inferred← citeshttps://doi.org/10.1016/j.dss.2014.12.001openalex-snapshot
Online profiling and clustering of Facebook users
inferred← citeshttps://doi.org/10.1016/j.ins.2022.05.097openalex-snapshot
Projected fuzzy C-means with probabilistic neighbors
inferred← citeshttps://doi.org/10.1016/j.jhealeco.2006.01.004openalex-snapshot
Modelling and estimation of valuations for the Dutch London Handicap Scale
inferred← citeshttps://doi.org/10.1016/j.tra.2003.08.002openalex-snapshot
A method for relating type of crash to traffic flow characteristics on urban freeways
inferred← citeshttps://doi.org/10.1061/(asce)0733-947x(2003)129:4(342)openalex-snapshot
Relationships Among Urban Freeway Accidents, Traffic Flow, Weather, and Lighting Conditions
inferred← citeshttps://doi.org/10.1080/00273170903504893openalex-snapshot
Simultaneous Two-Way Clustering of Multiple Correspondence Analysis
inferred← citeshttps://doi.org/10.1080/00273171.2016.1246996openalex-snapshot
Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis
inferred← citeshttps://doi.org/10.1080/10618600.2012.679237openalex-snapshot
Clustering Curves on a Reduced Subspace
inferred← citeshttps://doi.org/10.1080/10618600.2022.2035737openalex-snapshot
Visualizing Class Specific Heterogeneous Tendencies in Categorical Data
inferred← citeshttps://doi.org/10.1109/fuzzy.2006.1681798openalex-snapshot
Linear Fuzzy Clustering for Mixed Databases Based on Optimal Scaling
inferred← citeshttps://doi.org/10.1109/fuzzy.2007.4295398openalex-snapshot
Linear Fuzzy Clustering of Mixed Databases Based on Cluster-wise Optimal Scaling of Categorical Variables
inferred← citeshttps://doi.org/10.1109/scis-isis.2012.6505102openalex-snapshot
Cluster validation in k-Means clustering of mixed databases based on principal component analysis
inferred← citeshttps://doi.org/10.1111/j.1467-9868.2004.02059.xopenalex-snapshot
Clustering Objects on Subsets of Attributes (with Discussion)
inferred← citeshttps://doi.org/10.1155/jbb.2005.215openalex-snapshot
Finding Groups in Gene Expression Data
inferred← citeshttps://doi.org/10.1177/0008068318814630openalex-snapshot
Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
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/10.1177/075910639805800104openalex-snapshot
An Evaluation of Some Clustering Methods for Mixed Mode Variable Data Sets
inferred← citeshttps://doi.org/10.1201/b13101-9openalex-snapshot
References
inferred← citeshttps://doi.org/10.1207/s15327663jcp1001&2_09openalex-snapshot
Other Multivariate Techniques
inferred← citeshttps://doi.org/10.1214/lnms/1215454159openalex-snapshot
$L\sb 1$ and $L\sb 2$ approximation clustering for mixed data: scatter decompositions and algorithms
inferred← citeshttps://doi.org/10.1214/ss/1028905828openalex-snapshot
The Gifi system of descriptive multivariate analysis
inferred← citeshttps://doi.org/10.1300/j142v04n01_06openalex-snapshot
Market Segmentation Involving Mixtures of Quantitative and Qualitative Variables
inferred← citeshttps://doi.org/10.1348/000711005x48266openalex-snapshot
K‐means clustering: A half‐century synthesis
inferred← citeshttps://doi.org/10.17713/ajs.v27i3.535openalex-snapshot
Technik und Weiterentwicklung der multiplen Korrespondenzanalyse
inferred← citeshttps://doi.org/10.18637/jss.v091.i10openalex-snapshot
Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R
inferred← citeshttps://doi.org/10.18778/7525-695-6openalex-snapshot
Metody selekcji zmiennych w analizie skupień. Nowe procedury
inferred← citeshttps://doi.org/10.20965/jaciii.2007.p0162openalex-snapshot
FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification
inferred← citeshttps://doi.org/10.2333/bhmk.27.15openalex-snapshot
Growth Curve Representation and Clustering Under Optimal Scaling of Repeated Choice Data
inferred← citeshttps://doi.org/10.2333/bhmk.35.35openalex-snapshot
Clustering Nominal data with Equivalent Categories
inferred← citeshttps://doi.org/10.2333/bhmk.37.111openalex-snapshot
Fuzzy Cluster Multiple Correspondence Analysis
inferred← citeshttps://doi.org/10.3156/jsoft.19.287openalex-snapshot
Local Principal Component Analysis for Mixed Databases Based on Linear Fuzzy Clustering
inferred← citeshttps://doi.org/10.3389/frai.2022.1055294openalex-snapshot
Qluster: An easy-to-implement generic workflow for robust clustering of health data
inferred← citeshttps://doi.org/10.4135/9780857020994.n11openalex-snapshot
Correspondence Analysis, Multiple Correspondence Analysis, and Recent Developments
inferred← citeshttps://doi.org/10.48550/arxiv.1809.10496openalex-snapshot
Benchmarking in cluster analysis: A white paper
inferred← citeshttps://doi.org/10.48550/arxiv.1811.01596openalex-snapshot
Visualizing Class Specific Heterogeneous Tendencies in Categorical Data
inferred← citeshttps://doi.org/10.70675/c7a2bbf3z742az40fcza856z0f8d400c4ec7openalex-snapshot
Post-selection inference for transcriptomic data analysis
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