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

Exploratory Data Mining and Data Cleaning

2006 ◽  
Vol 101 (473) ◽  
pp. 399-399 ◽  
Author(s):  
Alan F Karr
Keyword(s):  
Data Mining ◽  
Data Cleaning ◽  
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.

Exploratory data mining and analysis using CONQUEST

2002 ◽  
Author(s):  
E. Mesrobian ◽  
R.R. Muntz ◽  
E.C. Shek ◽  
J.R. Santos ◽  
J. Yi ◽  
...  
Keyword(s):  
Data Mining ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Data Mining And Analysis

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 Exploratory Data Mining and Data Cleaning

2004 ◽  
Vol 11 (Book Review 9) ◽  
Author(s):  
Nicholas J. Cox
Keyword(s):  
Data Mining ◽  
Data Cleaning ◽  
Exploratory Data ◽  
Exploratory Data Mining

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.

Sequences, Nets, and Filters of Fuzzy Soft Multi Sets in Fuzzy Soft Multi Topological Spaces

2016 ◽  
pp. 172-190
Author(s):  
Anjan Mukherjee ◽  
Ajoy Kanti Das
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cluster Analysis ◽  
Topological Spaces ◽  
Main Task ◽  
Filter Convergence ◽  
Common Technique ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Convergence Sequence

In this chapter, the authors introduce a new sequence of fuzzy soft multi sets in fuzzy soft multi topological spaces and their basic properties are studied. The concepts of subsequence, convergence sequence and cluster fuzzy soft multi sets of fuzzy soft multi sets are proposed. Actually Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining and a common technique for statistical data analysis used in many fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics. Here the authors define the notions of net and filter and establish the correspondence between net convergence and filter convergence in fuzzy soft multi topological spaces.

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