PCA for heterogeneous data sets in a distributed data mining

2011 ◽  
Author(s):  
E. Chandra ◽  
P. Ajitha
Keyword(s):  
Data Mining ◽  
Heterogeneous Data ◽  
Distributed Data Mining ◽  
Data Sets ◽  
Distributed Data

scholarly journals A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms

2015 ◽  
pp. 43-53 ◽  
Author(s):  
Riyaz Sikora ◽  
O'la Al-Laymoun
Keyword(s):  
Data Mining ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Distributed Data Mining ◽  
Data Repository ◽  
Data Sets ◽  
Distributed Data ◽  
Ensemble Machine Learning ◽  
Data Scaling ◽  
The Individual

Distributed data mining and ensemble learning are two methods that aim to address the issue of data scaling, which is required to process the large amount of data collected these days. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by combining several classifiers created on the same data and improve their performance. In this chapter, the authors use concepts from both of these fields to create a modified and improved version of the standard stacking ensemble learning technique by using a Genetic Algorithm (GA) for creating the meta-classifier. They test the GA-based stacking algorithm on ten data sets from the UCI Data Repository and show the improvement in performance over the individual learning algorithms as well as over the standard stacking algorithm.

A Windowing strategy for Distributed Data Mining optimized through GPUs

2017 ◽  
Vol 93 ◽  
pp. 23-30 ◽  
Author(s):  
Xavier Limón ◽  
Alejandro Guerra-Hernández ◽  
Nicandro Cruz-Ramírez ◽  
Héctor-Gabriel Acosta-Mesa ◽  
Francisco Grimaldo
Keyword(s):  
Data Mining ◽  
Distributed Data Mining ◽  
Distributed Data
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