Data to Knowledge-Based Transformation: The Association Rules With Rapid Miner Approach and Predictive Analysis in Evergreen IT-Business Routines of PT Chevron Pacific Indonesia

Fauzan Asrin, S. Saide, Silvia Ratna
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引用次数: 2

Abstract

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.
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数据到知识的转换:基于快速挖掘方法的关联规则和预测分析在雪佛龙太平洋印尼分公司的常青it业务例程中
本研究的目的是分析大量的数据,这些数据似乎可以创建一个知识库,可以被公司用来增强他们的决策支持系统。作者将关联规则与快速挖掘软件、数据挖掘方法和包含各种数据探索场景的预测分析相结合。该研究为数据挖掘方法在工业领域的应用及其优缺点提供了重要依据。雪佛龙太平洋印度尼西亚(CPI)有一种类型的计算机维护活动。目前,由于计算机维护的准确性问题,经常发生大量的错误,对生产结果有很大的影响。因此,本研究侧重于使用增长模式的关联规则,这些模式经常出现在已确定为算法(FP-growth)的变量上,从而产生具有100%置信度值和97%支持值的知识。本研究的价值结果显示,支持和信任将成为高层管理人员决定常青it业务流程的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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