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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Improved RElim and FIN algorithm for frequent items generation
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Improved RElim and FIN algorithm for frequent items generation

机译：改进的RElim和FIN算法，用于频繁项生成

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摘要

Data mining is a process of extracting hidden information from large databases. Data mining is basically focused on many areas like - communication, retail, Financial, and marketing organizations. It determines relationships among internal and external factors. Association rule is a method for identifying the relations between variables in large databases. It is determined to discover frequent patterns, identify rules and strong rules discovered in databases. The main objective of this research work is to find accurate and large number of frequent itemset by enhancing existing algorithms like FIN and RElim algorithm, frequent patterns are generated and strong rules are identified. Normally in Association rule mining common threshold value is given to find the frequent itemset but in the enhanced algorithms individual threshold values are given to every item in the transactional database to find out the frequent items. From the analysis it was observed that enhanced RElim algorithm gives best results than enhanced FIN algorithm.
机译：数据挖掘是从大型数据库中提取隐藏信息的过程。数据挖掘基本上集中在许多领域，例如-通信，零售，金融和营销组织。它确定内部和外部因素之间的关系。关联规则是一种用于识别大型数据库中变量之间关系的方法。决心发现频繁的模式，识别规则和数据库中发现的强规则。这项研究工作的主要目的是通过增强现有算法（如FIN和RElim算法）来找到大量准确的频繁项集，并生成频繁模式并确定强规则。通常，在关联规则中，挖掘通用阈值是为了找到频繁项目集，而在增强算法中，会为事务数据库中的每个项目都赋予单个阈值，以找出频繁项目。从分析中可以看出，增强型RElim算法比增强型FIN算法提供了最佳结果。

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