Unsupervised Learning for Large Scale Data: The ATHLOS Project
Barmpas, P.; Tasoulis, S.; Vrahatis, A. G.; Prina, M.; Ayuso-Mateos, J. L.; Bickenbach, J.; Bayes, I.; Bobak, M.; Caballero, F. F.; Chatterji, S.; Egea-Cortes, L.; Garcia-Esquinas, E.; Leonardi, M.; Koskinen, S.; Koupil, I.; Pajak, A.; Prince, M. J.; Sanderson, W.; Scherbov, S.; Tamosiunas, A.; Galas, A.; Haro, J. M.; Sanchez-Niubo, A.; Plagianakos, V.; Panagiotakos, D.
Show abstract
1Recent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Graph Neural Network Modelling as a potentially effective Method for predicting and analyzing Procedures based on Patient Diagnoses 95%
- Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation 94%
- AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques 92%
Similar papers in this journal
- A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation 91%
- DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression 91%
- MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.