Large-scale data mining using genetics-based machine learning

2013 ◽  
Vol 3 (1) ◽  
pp. 37-61 ◽  
Author(s):  
Jaume Bacardit ◽  
Xavier Llorà
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

2019 ◽  
Vol 52 (1) ◽  
pp. 77-124 ◽  
Author(s):  
Giang Nguyen ◽  
Stefan Dlugolinsky ◽  
Martin Bobák ◽  
Viet Tran ◽  
Álvaro López García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Large Scale ◽  
Large Scale Data ◽  
Learning Frameworks ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers

2018 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Kislaya Kunjan ◽  
Huanmei Wu ◽  
Tammy R. Toscos ◽  
Bradley N. Doebbeling
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Health Centers ◽  
Patient Centered ◽  
Large Scale Data ◽  
Scale Data
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