Practical Data Mining: Lessons-Learned From the Barnett Shale of North Texas

2011 ◽  
Author(s):  
Randy F. LaFollette ◽  
William David Holcomb
Keyword(s):  
Data Mining ◽  
Lessons Learned ◽  
Barnett Shale ◽  
North Texas

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals Editorial: Data Mining Lessons Learned

2004 ◽  
Vol 57 (1/2) ◽  
pp. 5-11 ◽  
Author(s):  
Nada Lavrač ◽  
Hiroshi Motoda ◽  
Tom Fawcett
Keyword(s):  
Data Mining ◽  
Lessons Learned

Lessons Learned From Data Mining, Decision Support and Collaboration

2003 ◽  
pp. 237-246
Author(s):  
Dunja Mladenić ◽  
Nada Lavrač ◽  
Marko Bohanec
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Lessons Learned

New Cutter Technology Redefining PDC Durability Standards for Directional Control: North Texas/Barnett Shale

2010 ◽  
Author(s):  
Ryan Neil Baker ◽  
Yuelin Shen ◽  
John Zhang ◽  
Scott David Robertson
Keyword(s):  
Barnett Shale ◽  
North Texas ◽  
Directional Control

Lessons learned from contests in data mining

2011 ◽  
Author(s):  
Charles Elkan ◽  
Jeremy Howard ◽  
Yehuda Koren ◽  
Tie-Yan Liu ◽  
Claudia Perlich
Keyword(s):  
Data Mining ◽  
Lessons Learned

scholarly journals Exploring Disparities in Maternal Residential Proximity to Unconventional Gas Development in the Barnett Shale in North Texas

2019 ◽  
Vol 16 (3) ◽  
pp. 298
Author(s):  
Jennifer Ish ◽  
Elaine Symanski ◽  
Kristina Whitworth
Keyword(s):  
Low Income ◽  
White Women ◽  
Negative Binomial ◽  
Secondary Analysis ◽  
Negative Binomial Regression ◽  
Barnett Shale ◽  
North Texas ◽  
Unconventional Gas ◽  
Residential Proximity ◽  
Unconventional Gas Development

Background: This study explores sociodemographic disparities in residential proximity to unconventional gas development (UGD) among pregnant women. Methods: We conducted a secondary analysis using data from a retrospective birth cohort of 164,658 women with a live birth or fetal death from November 2010 to 2012 in the 24-county area comprising the Barnett Shale play, in North Texas. We considered both individual- and census tract-level indicators of sociodemographic status and computed Indexes of Concentration at the Extremes (ICE) to quantify relative neighborhood-level privilege/disadvantage. We used negative binomial regression to investigate the relation between these variables and the count of active UGD wells within 0.8 km of the home during gestation. We calculated count ratios (CR) and 95% confidence intervals (CI) to describe associations. Results: There were fewer wells located near homes of women of color living in low-income areas compared to non-Hispanic white women living in more privileged neighborhoods (ICE race/ethnicity + income: CR = 0.51, 95% CI = 0.48–0.55). Conclusions: While these results highlight a potential disparity in residential proximity to UGD in the Barnett Shale, they do not provide evidence of an environmental justice (EJ) issue nor negate findings of environmental injustice in other regions.

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