01647nas a2200229   4500000000100000000000100001008004100002260001200043653001600055653000800071653002200079653002300101100001900124700002700143700002800170245014400198856009600342300001000438490000600448520094900454022001401403       2019                            d  c06/201910aData Mining10aDSS10aAssociation Rules10aCellular Automaton1 aBaghdad Atmani1 aFatima Zohra Benhacine1 aFawzia Zohra Abdelouhab00aContribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/09/ijimai_5_5_5_pdf_16533.pdf  a38-470 v53 aIn the present paper we aim to study the visual decision support based on Cellular machine CASI (Cellular Automata for Symbolic Induction). The purpose is to improve the visualization of large sets of association rules, in order to perform Clinical decision support system and decrease doctors’ cognitive charge. One of the major problems in processing association rules is the exponential growth of generated rules volume which impacts doctor’s adaptation. In order to clarify it, many approaches meant to represent this set of association rules under visual context have been suggested. In this article we suggest to use jointly the CASI cellular machine and the colored 2D matrices to improve the visualization of association rules. Our approach has been divided into four important phases: (1) Data preparation, (2) Extracting association rules, (3) Boolean modeling of the rules base (4) 2D visualization colored by Boolean inferences.  a1989-1660