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基于C4.5算法的输差分析在数字气田中的应用
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2009-06-18
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在天然气输差分析的研究中，输差原因错综复杂，但在其影响因素数据中许多潜在有价值的规律未被发现。在各种机器学习算法中,决策树以其简单容易实现等特点被认可。本文首先介绍了分类器的基本概念和决策树构建思路，然后讲述了在天然气信息数据库的基础上如何建立决策树分类器，接着利用xml 的优越性进行存储，并依据创建
的决策树对数差进行预测。
关键词 天然气数据 输差分析 数据挖掘 决策树 C4.5 Xml
Abstract In the studies of analysis on measuring error of natural gas ,reason of measuring error is complex, but the potential and valuable rules hid in the result data have not been discovered. The algorithm of decision trees is well known due to simpleness and easy to realize in machine learning.Firstly we describe how to construct a decision tree classifier on the information database made by natural gas corporation,then store it taking advantage of xml,and forecast measuring error by the desion tree created.
Keywords natural gas data analysis on measuring error Data mining Decision tree C4.5 Xml
的决策树对数差进行预测。
关键词 天然气数据 输差分析 数据挖掘 决策树 C4.5 Xml
Abstract In the studies of analysis on measuring error of natural gas ,reason of measuring error is complex, but the potential and valuable rules hid in the result data have not been discovered. The algorithm of decision trees is well known due to simpleness and easy to realize in machine learning.Firstly we describe how to construct a decision tree classifier on the information database made by natural gas corporation,then store it taking advantage of xml,and forecast measuring error by the desion tree created.
Keywords natural gas data analysis on measuring error Data mining Decision tree C4.5 Xml
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