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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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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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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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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Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning [PDF]
IEEE Access, 2015 Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living ...
Dao Lam, Mingzhen Wei, Donald Wunsch
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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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Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications [PDF]
MathematicsThis study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved ...
Cinzia Di Nuzzo
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A deep learning mixed-data type approach for the classification of FHR signals [PDF]
Frontiers in Bioengineering and Biotechnology, 2022 The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals.
Edoardo Spairani +3 more
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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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