Myth 11: Need More Data for Discrete Data than Continuous Data Analysis

2015 ◽  
pp. 111-122
Keyword(s):  
Data Analysis ◽  
Discrete Data ◽  
Continuous Data

Attribute Reduction Based on Continuous Attribute Domain

2014 ◽  
Vol 1044-1045 ◽  
pp. 1084-1087
Author(s):  
Yi Zhang
Keyword(s):  
Feature Extraction ◽  
Attribute Reduction ◽  
Real Life ◽  
Discrete Data ◽  
General Algorithm ◽  
Continuous Data ◽  
Brain Waves ◽  
Continuous Eeg ◽  
Continuous Attribute

Discrete data attributes reduction, there are many mature methods, but for continuous data attributes reduction, general algorithm is not very good, in real life, the continuous data feature extraction and discrete data is also important, based on the number of new brain waves as analysis object, and through the continuous eeg feature extraction comparison, verify the feasibility of the proposed method.

Discrete Data Analysis with R

2015 ◽  
Author(s):  
Michael Friendly ◽  
David Meyer ◽  
Achim Zeileis
Keyword(s):  
Data Analysis ◽  
Discrete Data
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