Exploratory data mining using CART in the behavioral sciences.

2012 ◽  
pp. 405-421 ◽  
Author(s):  
John J. McArdle
Keyword(s):  
Data Mining ◽  
Behavioral Sciences ◽  
Exploratory Data ◽  
Exploratory Data Mining

Impediments to Exploratory Data Mining Success

2008 ◽  
pp. 2566-2582
Author(s):  
Jeff Zeanah
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Exploratory Data ◽  
Exploratory Data Mining

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.

scholarly journals Exploratory Data Mining Analysis Identifying Subgroups of Patients With Depression Who Are at High Risk for Suicide

2009 ◽  
Vol 70 (11) ◽  
pp. 1495-1500 ◽  
Author(s):  
Mark A. Ilgen ◽  
Karen Downing ◽  
Kara Zivin ◽  
Katherine J. Hoggatt ◽  
H. Myra Kim ◽  
...  
Keyword(s):  
Data Mining ◽  
High Risk ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Data Mining Analysis

scholarly journals Development and validation of empirically derived frequency criteria for NSSI disorder using exploratory data mining.

2017 ◽  
Vol 29 (2) ◽  
pp. 221-231 ◽  
Author(s):  
Brooke A. Ammerman ◽  
Ross Jacobucci ◽  
Evan M. Kleiman ◽  
Jennifer J. Muehlenkamp ◽  
Michael S. McCloskey
Keyword(s):  
Data Mining ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Development And Validation ◽  
Nssi Disorder

Impediments to Exploratory Data Mining Success

2011 ◽  
pp. 280-299
Author(s):  
Jeff Zeanah
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Exploratory Data ◽  
Exploratory Data Mining

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.

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