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Predicting the Credibility of Market Basket Analysis by using Error Estimating techniques: Application of Micro Ingredient Manufacturing Sales Data.

机译：通过使用误差估计技术来预测市场篮子分析的可信度：微量成分制造销售数据的应用。

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摘要

When one is data mining a small data base, employing cross-validation techniques can show the credibility of the results. Cross-validation is a method of partitioning data into smaller segments in order to increase the quality of a small dataset. Typically the dataset is divided into two partitions. The first partition, usually two thirds of the dataset, is set aside for training. The training partition is used to find the best method to use for this particular dataset. The training partition is further divided into several equal size folds; typically ten folds are adequate. Then each fold is process N number of times, after which the results are combined and then averaged to give one final result. Leave-one-out and bootstrap are techniques of cross-validation in that data is partitioned into smaller units; or folds. Bootstrap employs the statistical process of sampling with replacement while leave-one-out uses N fold just as cross-validation. In This thesis we compare techniques and results of leave-one-out and bootstrap methods with a combined bootstrapleave-one-out method to find the best training data for a market basket analysis of a small manufacturing business database.
机译：当人们在挖掘一个小型数据库时，采用交叉验证技术可以证明结果的可靠性。交叉验证是一种将数据划分为较小段以提高小型数据集质量的方法。通常，数据集分为两个分区。通常将数据集的三分之二的第一个分区留出进行训练。训练分区用于找到用于此特定数据集的最佳方法。训练分区进一步分为几个相等大小的折叠；通常十倍就足够了。然后将每折处理N次，然后将结果合并，然后取平均值，得到一个最终结果。留一法和引导法是交叉验证的技术，因为数据被分成较小的单元。或褶皱。 Bootstrap采用抽样替换的统计过程，而留一法则使用N倍作为交叉验证。在这篇论文中，我们比较了留一法和引导法与引导一过法一法相结合的技术和结果，以找到用于小型制造企业数据库的市场分析的最佳培训数据。

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  • 作者

    Belongia, Dwayne.;

  • 作者单位

    University of Nebraska at Omaha.;

  • 授予单位 University of Nebraska at Omaha.;
  • 学科 Computer Science.
  • 学位 M.S.
  • 年度 2012
  • 页码 65 p.
  • 总页数 65
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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