International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Oct. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 79
Paper Published : 1191
No. of Authors : 2992
  Journal Paper

Paper Title
Exploring Causal Relations In Data Mining By Using Directed Acyclic Graphs (DAG)

Abstract
Discovering causal pattern among the set of variables in a database is the primary task of data mining thereby well predicted models can be developed for decision making. This research article offers a review of open source and easy to use software package Tetrad used to explore the causal relations in terms of a diagram directed acyclic graph (DAG). Causal modeling through directed acyclic graphs is a powerful analytic tool that explores the cause and effect relationship between study variables especially in the time series data that encompass of higher variations. DAG modeling can be seen as an alternative to many of the statistical procedures like path analysis or structural equation modeling (SEM). Keywords- Causal Modeling, Decision Making, Directed Acyclic Graph, Tetrad


Author - Krishna Murthy Inumula

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| Published on 2015-05-07
   
   
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