Results 11 to 20 of about 4,692,865 (188)
Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning
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
doaj +3 more sources
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
doaj +1 more source
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
doaj +1 more source
Applications of Clustering with Mixed Type Data in Life Insurance
Risks, 2021 Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims.
Shuang Yin +3 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
kamila: Clustering Mixed-Type Data in R and Hadoop
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
doaj +1 more source
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 ...
Best, Nicky +2 more
core +2 more sources
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
doaj +1 more source