Clustering of samples and variables with mixed-type data. [PDF]
PLoS ONE, 2017 Analysis of data measured on different scales is a relevant challenge. Biomedical studies often focus on high-throughput datasets of, e.g., quantitative measurements.
Manuela Hummel +2 more
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Spectral Clustering of Mixed-Type Data [PDF]
Stats, 2021 Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups.
Felix Mbuga, Cristina Tortora
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Missing-Values Adjustment for Mixed-Type Data [PDF]
Journal of Probability and Statistics, 2011 We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record.
Agostino Tarsitano, Marianna Falcone
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A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning [PDF]
Entropy, 2020 The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem’s features belong to a numerical space.
Ivan Lopez-Arevalo +5 more
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Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data [PDF]
Frontiers in Big Data, 2021 AI-based data synthesis has seen rapid progress over the last several years and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing.
Michael Platzer, Thomas Reutterer
doaj +4 more sources
Learning clinical networks from medical records based on information estimates in mixed-type data. [PDF]
PLoS Computational Biology, 2020 The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess.
Vincent Cabeli +5 more
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MissForest - nonparametric missing value imputation for mixed-type data [PDF]
Bioinformatics, 2011 Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. J. Stekhoven +11 more
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A modified and weighted Gower distance-based clustering analysis for mixed type data: a simulation and empirical analyses [PDF]
BMC Medical Research MethodologyBackground Traditional clustering techniques are typically restricted to either continuous or categorical variables. However, most real-world clinical data are mixed type.
Pinyan Liu +5 more
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Clustering Approaches for Mixed-Type Data: A Comparative Study
Journal of Probability and StatisticsClustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suited for this task. This study presents the state-of-
Badih Ghattas, Alvaro Sanchez San-Benito
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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors [PDF]
BMC Medical Research MethodologyBackground Identifying and understanding disease risk factors is crucial in epidemiology, particularly for chronic and noncommunicable diseases that often have complex interrelationships.
Yuanyuan Zhao, Jinzhu Jia
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