Open AccessMapper Association Rule Reducer Mining Method (MARRMM) for the Diagnosis of Heart Disease Using Hesitation Rule Set
Author(s) -
P. Umasankar
Publication year - 2019
Publication title -
asian journal of managerial science/asian journal of managerial science
Language(s) - English
Resource type - Journals
eISSN - 2583-9810
pISSN - 2249-6300
DOI - 10.51983/ajes-2019.8.1.2338
Subject(s) - association rule learning , reducer , computer science , data mining , set (abstract data type) , association (psychology) , rule based system , artificial intelligence , machine learning , psychology , engineering , civil engineering , psychotherapist , programming language
Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, in the third work, a novel hesitation rule generation method has proposed by blending the Map Reduce concept and Association Rule Mining. In this Mapper Association Rule Reducer Mining method has proposed to generate the hesitation rule set for giving the appropriate medication to the patient who are considered as not getting heart disease.