Results 21 to 30 of about 4,503,666 (319)
Frontiers in Neurology, 2021 We aimed to (1) apply cluster analysis techniques to mixed-type data (numerical and categorical) from baseline neuropsychological standard and widely used assessments of patients with acquired brain injury (ABI) (2) apply state-of-the-art cluster ...
Alejandro García-Rudolph +18 more
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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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Distance Metrics and Clustering Methods for Mixed‐type Data
International Statistical Review, 2018 SummaryIn spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed‐type data, we use both theoretical and empirical analyses to present a critical review ...
Alexander H. Foss +2 more
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Mixed-type data generation method based on generative adversarial networks
EURASIP Journal on Wireless Communications and Networking, 2022 Data-driven based deep learing has become a key research direction in the field of artificial intelligence. Abundant training data is a guarantee for building efficient and accurate models.
Ning Wei +5 more
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Applied Sciences, 2022 The rarity of equipment failures results in a high level of imbalance between failure data and normal operation data, which makes the effective classification and prediction of such data difficult.
Cheng-Hui Chen +2 more
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Continuous Diffusion for Mixed-Type Tabular Data
, 2023 Score-based generative models, commonly referred to as diffusion models, have proven to be successful at generating text and image data. However, their adaptation to mixed-type tabular data remains underexplored. In this work, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data.
Mueller, Markus +2 more
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A deep learning mixed-data type approach for the classification of FHR signals
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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Model-based co-clustering for mixed type data
Computational Statistics & Data Analysis, 2020 The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been developed for simultaneously producing groups of observations and features.By grouping the data set into blocks (the crossing of a row-
Margot Selosse +3 more
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A real data-driven simulation strategy to select an imputation method for mixed-type trait data.
PLoS Computational Biology, 2023 Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for ...
Jacqueline A May +2 more
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IEEE Access, 2022 In Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) manufacturing, conducting a machine learning based system with multiple data types has become actively desired to solve complicated problems.
Yi Liu, Hsueh-Ping Lu, Ching-Hao Lai
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