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
doaj +6 more sources
Bayesian Non-Parametric Models for Spatially Indexed Data of Mixed Type [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014 We develop Bayesian nonparametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions ...
Γεώργιος Παπαγεωργίου +2 more
core +9 more sources
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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kamila: Clustering Mixed-Type Data in R and Hadoop [PDF]
Journal of Statistical Software, 2018 In this paper we discuss the challenge of equitably combining continuous (quantitative) and categorical (qualitative) variables for the purpose of cluster analysis. Existing techniques require strong parametric assumptions, or difficult-to-specify tuning
Alexander H. Foss, Marianthi Markatou
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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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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
doaj +2 more sources
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
doaj +2 more sources
Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification. [PDF]
Cancers (Basel), 2021 Quist J +3 more
europepmc +3 more sources
Pattern Classification for Mixed Feature-Type Symbolic Data Using Supervised Hierarchical Conceptual Clustering [PDF]
StatsThis paper describes a region-oriented method of pattern classification based on the Cartesian system model (CSM), a mathematical model that allows manipulating mixed feature-type symbolic data. We use the supervised hierarchical conceptual clustering to
Manabu Ichino, Hiroyuki Yaguchi
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