Results 41 to 50 of about 1,816,247 (166)
Analysis of Categorical Data with the R Package confreq
Psych, 2021 The person-centered approach in categorical data analysis is introduced as a complementary approach to the variable-centered approach. The former uses persons, animals, or objects on the basis of their combination of characteristics which can be ...
Jörg-Henrik Heine, Mark Stemmler
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The Dirichet-Multinomial model for multivariate randomized response data and small samples [PDF]
, 2012 In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when ...
Avetisyan, M., Fox, G.J.A.
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Journal of Computing and Information Technology, 2016 In recent years, materialized views (MVs) are widely used to enhance the database performance by storing pre-calculated results of resource-intensive queries in the physical memory.
Kateryna Novokhatska, Oleksii Kungurtsev
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Tree-Based Contrast Subspace Mining for Categorical Data
International Journal of Computational Intelligence Systems, 2020 Mining contrast subspace has emerged to find subspaces where a particular queried object is most similar to the target class against the non-target class in a two-class data set.
Florence Sia, Rayner Alfred, Yuto Lim
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MCF Tree-Based Clustering Method for Very Large Mixed-Type Data Set
IEEE Access, 2021 Several clustering methods have been proposed for analyzing numerous mixed-type data sets composed of numeric and categorical attributes. However, existing clustering methods are not suitable for clustering very large mixed-type data sets because they ...
Hyeong-Cheol Ryu, Sungwon Jung
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MCMC for Imbalanced Categorical Data
, 2017 Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
Dunson, David B. +3 more
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Heterogeneous Graph Based Similarity Measure for Categorical Data Unsupervised Learning
IEEE Access, 2019 Different from numerical attributes, measuring the similarity between categorical attributes is more complex due to their non-inherently ordered characteristic, especially in an unsupervised scheme.
Yanqing Ye +4 more
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Fuzzy Soft Set Clustering for Categorical Data
JOIV: International Journal on Informatics VisualizationCategorical data clustering is difficult because categorical data lacks natural order and can comprise groups of data only related to specific dimensions. Conventional clustering, such as k-means, cannot be openly used to categorical data.
Iwan Tri Riyadi Yanto +5 more
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A new specification of generalized linear models for categorical data [PDF]
, 2014 Regression models for categorical data are specified in heterogeneous ways. We propose to unify the specification of such models. This allows us to define the family of reference models for nominal data.
Guédon, Yann +2 more
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An Improved Count-Based Classifier for Categorical Data
IEEE AccessThe classification of categorical data is a fundamental task in machine learning, with numerous algorithms and techniques available. However, existing approaches often face challenges related to interpretability, scalability, and handling sparse or ...
Sanskriti Sanjay Kumar Singh +1 more
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