基于犹豫规则集的Mapper关联规则减速器挖掘方法(MARRMM

P. Umasankar
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引用次数: 0

摘要

关联规则是数据挖掘中的主要任务之一,用于发现事务数据库中项目之间的相关性。大多数垂直和水平关联规则挖掘算法都是为了改进频繁项发现步骤而开发的,这对训练时间和内存使用要求很高,特别是在输入数据库非常大的情况下。在本文的第三篇文章中,提出了一种将Map Reduce概念与关联规则挖掘相结合的犹豫规则生成方法。在此Mapper中,提出了关联规则减速器挖掘方法,生成犹豫规则集,为被认为没有心脏病的患者提供适当的药物治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapper Association Rule Reducer Mining Method (MARRMM) for the Diagnosis of Heart Disease Using Hesitation Rule Set
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.
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