Title :
EDA and ML -- A Perfect Pair for Large-Scale Data Analysis
Author :
Hafen, Ryan ; Critchlow, T.
Author_Institution :
Pacific Northwest Nat. Lab., Richland, WA, USA
Abstract :
In this position paper, we discuss how Exploratory Data Analysis (EDA) and Machine Learning (ML) can work together in large-scale data analysis environments. In particular, we describe how applying EDA techniques and ML methods in a complementary fashion can be used to address some of the challenges faced when applying ML techniques to large, real world data sets, and discuss tools that help do the job. This iterative approach is demonstrated with a simple example of how extracting events from a historical sensor data set was enabled by iteratively identifying and filtering various types of erroneous data.
Keywords :
data analysis; learning (artificial intelligence); EDA; ML; erroneous data filtering; exploratory data analysis; large-scale data analysis; machine learning; Algorithm design and analysis; Analytical models; Data analysis; Data mining; Data models; Data visualization; Machine learning algorithms; exploratory data analyisis; large-data analysis; machine learning;
Conference_Titel :
Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2013 IEEE 27th International
Conference_Location :
Cambridge, MA
Print_ISBN :
978-0-7695-4979-8
DOI :
10.1109/IPDPSW.2013.118