Results 11 to 20 of about 4,674,587 (322)
Clustering Approaches for Mixed-Type Data: A Comparative Study [PDF]
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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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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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
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Bayesian nonparametric models for spatially indexed data of mixed type [PDF]
, 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
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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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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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Clustering Mixed-Type Data Using a Probabilistic Distance Algorithm
Applied Soft Computing, 2022 Cluster analysis is a broadly used unsupervised data analysis technique for finding groups of homogeneous units in a data set. Probabilistic distance clustering adjusted for cluster size (PDQ), discussed in this contribution, falls within the broad category of clustering methods initially developed to deal with continuous data; it has the advantage of ...
Cristina Tortora, Francesco Palumbo
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Clustering of Mixed-Type Data Considering Concept Hierarchies
, 2019 Most clustering algorithms have been designed only for pure numerical or pure categorical data sets while nowadays many applications generate mixed data. It arises the question how to integrate various types of attributes so that one could efficiently group objects without loss of information.
Sahar Behzadi +3 more
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Cluster Validation for Mixed-Type Data
, 2020 For cluster analysis based on mixed-type data (i.e. data consisting of numerical and categorical variables), comparatively few clustering methods are available. One popular approach to deal with this kind of problems is an extension of the k-means algorithm (Huang, 1998), the so-called k-prototype algorithm, which is implemented in the R package ...
Rabea Aschenbruck, Gero Szepannek
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