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Clustering criteria for discrete data and latent class models

https://doi.org/10.1007/bf02616237
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-67740-3_3openalex-snapshot
Clustering Analysis
inferredcites →https://doi.org/10.1007/978-3-642-75040-3_2openalex-snapshot
Probabilistic Aspects in Cluster Analysis
inferredcites →https://doi.org/10.1007/978-94-009-5564-6openalex-snapshot
An Introduction to Latent Variable Models
inferredcites →https://doi.org/10.1007/bf00940196openalex-snapshot
Local convergence analysis of a grouped variable version of coordinate descent
inferredcites →https://doi.org/10.1007/bf01896986openalex-snapshot
Parameter modification for clustering criteria
inferredcites →https://doi.org/10.1007/bf01901672openalex-snapshot
On characterizing optimization-based clustering methods
inferredcites →https://doi.org/10.1016/0167-9473(85)90008-8openalex-snapshot
Classification as a tool of research
inferredcites →https://doi.org/10.1016/0169-2070(93)90088-5openalex-snapshot
Journal of the Royal Statistical Society (B)
inferredcites →https://doi.org/10.1016/0377-2217(89)90016-7openalex-snapshot
Classification and related methods of data analysis
inferredcites →https://doi.org/10.1086/220785openalex-snapshot
Role Conflict and Personality
inferredcites →https://doi.org/10.1093/biomet/61.2.215openalex-snapshot
Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models
inferredcites →https://doi.org/10.1093/biomet/63.3.413openalex-snapshot
Multivariate Binary Discrimination by the Kernel Method
inferredcites →https://doi.org/10.1093/biomet/65.2.273openalex-snapshot
Asymptotic Behaviour of Classification Maximum Likelihood Estimates
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.2307/2347733openalex-snapshot
Classification and Mixture Approaches to Clustering via Maximum Likelihood
inferredcites →https://doi.org/10.2307/2529003openalex-snapshot
Clustering Methods Based on Likelihood Ratio Criteria
inferredcites →https://doi.org/10.2307/2529563openalex-snapshot
389: Separating Mixtures of Normal Distributions

What points at this work

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

inferred← citeshttps://doi.org/10.1002/(sici)1099-095x(199609)7:5<471::aid-env223>3.0.co;2-sopenalex-snapshot
A REVIEW OF POINT PATTERN METHODS FOR SPATIAL MODELLING OF EVENTS AROUND SOURCES OF POLLUTION
inferred← citeshttps://doi.org/10.1002/9780470382776.refsopenalex-snapshot
References
inferred← citeshttps://doi.org/10.1002/9780470611777.ch8openalex-snapshot
Clustering and the Mixture Model
inferred← citeshttps://doi.org/10.1002/9781118445112.stat07846openalex-snapshot
Dimension Reduction in Clustering
inferred← citeshttps://doi.org/10.1002/9781118649480.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/asm.3150090105openalex-snapshot
Binary clustering with missing data
inferred← citeshttps://doi.org/10.1002/sam.11502openalex-snapshot
Model‐based clustering of time‐dependent categorical sequences with application to the analysis of major life event patterns
inferred← citeshttps://doi.org/10.1006/jmva.1997.1687openalex-snapshot
Classification of Binary Vectors by Stochastic Complexity
inferred← citeshttps://doi.org/10.1007/978-3-540-70981-7_1openalex-snapshot
Mixture Models for Classification
inferred← citeshttps://doi.org/10.1007/978-3-642-02962-2_50openalex-snapshot
A Novel Multimodal Probability Model for Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-17534-3_38openalex-snapshot
Clustering Categorical Data Using an Extended Modularity Measure
inferred← citeshttps://doi.org/10.1007/978-3-642-55991-4_1openalex-snapshot
Clustering and Models
inferred← citeshttps://doi.org/10.1007/978-94-011-0800-3_4openalex-snapshot
Information and Entropy in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-981-13-0553-5_6openalex-snapshot
Model-Based Clustering
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/bf01246099openalex-snapshot
Numerical taxonomy and the principle of maximum entropy
inferred← citeshttps://doi.org/10.1007/bf01901672openalex-snapshot
On characterizing optimization-based clustering methods
inferred← citeshttps://doi.org/10.1007/bf02294491openalex-snapshot
Was Euclid an Unnecessarily Sophisticated Psychologist?
inferred← citeshttps://doi.org/10.1007/s00357-015-9180-4openalex-snapshot
Model-Based Clustering for Conditionally Correlated Categorical Data
inferred← citeshttps://doi.org/10.1007/s00357-016-9211-9openalex-snapshot
Model-Based Clustering
inferred← citeshttps://doi.org/10.1007/s00357-018-9301-yopenalex-snapshot
Variable Selection for Mixed Data Clustering: Application in Human Population Genomics
inferred← citeshttps://doi.org/10.1007/s00357-020-09369-yopenalex-snapshot
Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering
inferred← citeshttps://doi.org/10.1007/s00778-009-0134-5openalex-snapshot
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
inferred← citeshttps://doi.org/10.1007/s10115-008-0159-xopenalex-snapshot
“Best K”: critical clustering structures in categorical datasets
inferred← citeshttps://doi.org/10.1007/s10618-019-00635-1openalex-snapshot
Efficient mixture model for clustering of sparse high dimensional binary data
inferred← citeshttps://doi.org/10.1007/s10994-005-5316-9openalex-snapshot
A Unified View on Clustering Binary Data
inferred← citeshttps://doi.org/10.1007/s11222-014-9505-xopenalex-snapshot
Kernel discriminant analysis and clustering with parsimonious Gaussian process models
inferred← citeshttps://doi.org/10.1007/s11222-016-9670-1openalex-snapshot
Variable selection for model-based clustering using the integrated complete-data likelihood
inferred← citeshttps://doi.org/10.1007/s11222-022-10145-8openalex-snapshot
Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series
inferred← citeshttps://doi.org/10.1007/s11634-016-0250-1openalex-snapshot
Latent class model with conditional dependency per modes to cluster categorical data
inferred← citeshttps://doi.org/10.1007/s11634-021-00454-7openalex-snapshot
Model-based clustering for random hypergraphs
inferred← citeshttps://doi.org/10.1016/0167-9473(92)90042-eopenalex-snapshot
A classification EM algorithm for clustering and two stochastic versions
inferred← citeshttps://doi.org/10.1016/0167-9473(96)88919-5openalex-snapshot
Probabilistic models in cluster analysis
inferred← citeshttps://doi.org/10.1016/j.csda.2007.09.007openalex-snapshot
Block clustering with Bernoulli mixture models: Comparison of different approaches
inferred← citeshttps://doi.org/10.1016/j.csda.2009.07.013openalex-snapshot
On multivariate binary data clustering and feature weighting
inferred← citeshttps://doi.org/10.1016/j.csda.2016.05.018openalex-snapshot
Comparing classical criteria for selecting intra-class correlated features in Multimix
inferred← citeshttps://doi.org/10.1016/j.csda.2017.04.010openalex-snapshot
A family of block-wise one-factor distributions for modeling high-dimensional binary data
inferred← citeshttps://doi.org/10.1016/j.ejor.2003.07.004openalex-snapshot
Estimation of parameters in latent class models using fuzzy clustering algorithms
inferred← citeshttps://doi.org/10.1016/j.ins.2013.03.007openalex-snapshot
Clustering construction on a multimodal probability model
inferred← citeshttps://doi.org/10.1016/j.jspi.2010.03.042openalex-snapshot
Exact and Monte Carlo calculations of integrated likelihoods for the latent class model
inferred← citeshttps://doi.org/10.1016/j.jspi.2014.02.011openalex-snapshot
Model-based clustering for multivariate partial ranking data
inferred← citeshttps://doi.org/10.1016/j.tmp.2017.07.020openalex-snapshot
Event sustainability and transportation policy: A model-based cluster analysis for a cross-comparison of hallmark events
inferred← citeshttps://doi.org/10.1016/s0031-3203(02)00074-2openalex-snapshot
Clustering with block mixture models
inferred← citeshttps://doi.org/10.1016/s0165-0114(00)00053-1openalex-snapshot
Reconstruction of additive phylogenetic tree
inferred← citeshttps://doi.org/10.1016/s0167-9473(96)00021-7openalex-snapshot
Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data
inferred← citeshttps://doi.org/10.1017/9781108644181openalex-snapshot
Model-Based Clustering and Classification for Data Science
inferred← citeshttps://doi.org/10.1017/9781108644181.001openalex-snapshot
Preface
inferred← citeshttps://doi.org/10.1017/9781108644181.002openalex-snapshot
Introduction
inferred← citeshttps://doi.org/10.1017/9781108644181.003openalex-snapshot
Model-based Clustering: Basic Ideas
inferred← citeshttps://doi.org/10.1017/9781108644181.004openalex-snapshot
Dealing with Difficulties
inferred← citeshttps://doi.org/10.1017/9781108644181.005openalex-snapshot
Model-based Classification
inferred← citeshttps://doi.org/10.1017/9781108644181.006openalex-snapshot
Semi-supervised Clustering and Classification
inferred← citeshttps://doi.org/10.1017/9781108644181.007openalex-snapshot
Discrete Data Clustering
inferred← citeshttps://doi.org/10.1017/9781108644181.008openalex-snapshot
Variable Selection
inferred← citeshttps://doi.org/10.1017/9781108644181.009openalex-snapshot
High-dimensional Data
inferred← citeshttps://doi.org/10.1017/9781108644181.010openalex-snapshot
Non-Gaussian Model-based Clustering
inferred← citeshttps://doi.org/10.1017/9781108644181.011openalex-snapshot
Network Data
inferred← citeshttps://doi.org/10.1017/9781108644181.012openalex-snapshot
Model-based Clustering with Covariates
inferred← citeshttps://doi.org/10.1017/9781108644181.013openalex-snapshot
Other Topics
inferred← citeshttps://doi.org/10.1017/9781108644181.014openalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1017/9781108644181.015openalex-snapshot
Author Index
inferred← citeshttps://doi.org/10.1017/9781108644181.016openalex-snapshot
Subject Index
inferred← citeshttps://doi.org/10.1023/b:stco.0000039479.56180.d5openalex-snapshot
Fuzzy clustering algorithm for latent class model
inferred← citeshttps://doi.org/10.1051/eas/1677005openalex-snapshot
Supervised and Unsupervised Classification Using Mixture Models
inferred← citeshttps://doi.org/10.1080/01621459.2024.2402568openalex-snapshot
Model-Based Clustering of Categorical Data Based on the Hamming Distance
inferred← citeshttps://doi.org/10.1080/02664760902889957openalex-snapshot
Extension of model-based classification for binary data when training and test populations differ
inferred← citeshttps://doi.org/10.1080/03610918.2019.1588305openalex-snapshot
Automatic genetic algorithm in clustering for discrete elements
inferred← citeshttps://doi.org/10.1080/03610920903581010openalex-snapshot
A Clustering Method for Categorical Ordinal Data
inferred← citeshttps://doi.org/10.1080/03610926.2013.806665openalex-snapshot
The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach
inferred← citeshttps://doi.org/10.1080/10618600.2017.1305278openalex-snapshot
Clustering Categorical Data via Ensembling Dissimilarity Matrices
inferred← citeshttps://doi.org/10.1088/1742-6596/1538/1/012062openalex-snapshot
On the modeling of the new student acceptance status through science and technology written test using bernoulli mixture model
inferred← citeshttps://doi.org/10.1093/biomet/93.4.843openalex-snapshot
Building mixture trees from binary sequence data
inferred← citeshttps://doi.org/10.1109/ccnc.2009.4784914openalex-snapshot
Using Entropy Based Clustering Method to Improve the Utilization Heterogeneous Antennas in Ad Hoc Wireless Networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2016.7727497openalex-snapshot
Fast Entropy Clustering of sparse high dimensional binary data
inferred← citeshttps://doi.org/10.1109/isit.1994.394629openalex-snapshot
Clustering and quantization of binary vectors with stochastic complexity
inferred← citeshttps://doi.org/10.1109/tcomm.2023.3298977openalex-snapshot
Enhanced Error Correction Employing Natural Redundancy of Sensor Data
inferred← citeshttps://doi.org/10.1109/tkde.2011.201openalex-snapshot
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework
inferred← citeshttps://doi.org/10.1111/insr.70038openalex-snapshot
Linking the Comparison and Graphical Approaches to Bipartite Matching
inferred← citeshttps://doi.org/10.1111/j.1467-9876.2012.01066.xopenalex-snapshot
Discussion of Henning and Liao: How to find an appropriate clustering for mixed type variables with application to socio-economic stratification. Journal of the Royal Statistical Society: Series C. 62, 309-369
inferred← citeshttps://doi.org/10.1111/j.1751-5823.2001.tb00458.xopenalex-snapshot
Probabilistic Models for Bacterial Taxonomy
inferred← citeshttps://doi.org/10.1137/1.9781611972757.54openalex-snapshot
On Clustering Binary Data
inferred← citeshttps://doi.org/10.1137/1.9781611972764.49openalex-snapshot
Detecting the Change of Clustering Structure in Categorical Data Streams
inferred← citeshttps://doi.org/10.1142/s0218488504003193openalex-snapshot
FUZZY CLASS LOGISTIC REGRESSION ANALYSIS
inferred← citeshttps://doi.org/10.1145/1015330.1015404openalex-snapshot
Entropy-based criterion in categorical clustering
inferred← citeshttps://doi.org/10.1155/2018/2764016openalex-snapshot
Clustering for Probability Density Functions by New k -Medoids Method
inferred← citeshttps://doi.org/10.1177/0962280214549040openalex-snapshot
An alternative classification to mixture modeling for longitudinal counts or binary measures
inferred← citeshttps://doi.org/10.1186/s12859-016-1245-9openalex-snapshot
EnsCat: clustering of categorical data via ensembling
inferred← citeshttps://doi.org/10.1186/s40359-026-04178-1openalex-snapshot
The dynamics of personal accountability within the context of teacher self-regulation profiles
inferred← citeshttps://doi.org/10.1198/108571101300325111openalex-snapshot
Clustering mutational spectra via classification likelihood and markov chain monte carlo algorithms
inferred← citeshttps://doi.org/10.1201/9781315373577-12openalex-snapshot
References
inferred← citeshttps://doi.org/10.1214/13-ejs844openalex-snapshot
Clustering and variable selection for categorical multivariate data
inferred← citeshttps://doi.org/10.1255/jsi.2016.a3openalex-snapshot
Model-based co-clustering for hyperspectral images
inferred← citeshttps://doi.org/10.1515/demo-2015-0017openalex-snapshot
A classification method for binary predictors combining similarity measures and mixture models
inferred← citeshttps://doi.org/10.18637/jss.v067.i06openalex-snapshot
Rmixmod: TheRPackage of the Model-Based Unsupervised, Supervised, and Semi-Supervised ClassificationMixmodLibrary
inferred← citeshttps://doi.org/10.31274/etd-180810-3100openalex-snapshot
A Hierarchical Clustering and Validity Index for Mixed Data
inferred← citeshttps://doi.org/10.3233/fi-2011-555openalex-snapshot
A Novel Multimodal Probability Model for Cluster Analysis
inferred← citeshttps://doi.org/10.48550/arxiv.1402.5103openalex-snapshot
Finite mixture model of conditional dependencies modes to cluster categorical data
inferred← citeshttps://doi.org/10.48550/arxiv.1511.01343openalex-snapshot
A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data
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