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    • 11. 发明申请
    • METHOD AND COMPUTER PROGRAM PRODUCT FOR USING DATA MINING TOOLS TO AUTOMATICALLY COMPARE AN INVESTIGATED UNIT AND A BENCHMARK UNIT
    • 使用数据挖掘工具自动比较被调查单位和基准单位的方法和计算机程序产品
    • US20110231444A1
    • 2011-09-22
    • US13117229
    • 2011-05-27
    • James Howard Drew
    • James Howard Drew
    • G06F17/30
    • G06Q90/00G06Q10/0639Y10S707/99936
    • Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.
    • 业务交易中的运营问题来源常常显示在相对较小的数据中,这就是所谓的故障热点。 从内部公司交易数据中识别这些热点通常是问题解决的基本步骤,但是由于大量的事务和大量的事务变量要分析,这个分析过程非常复杂。 提供了一套实用的修改,用于数据挖掘技术和逻辑回归,以定制他们查找故障热点。 因此,这种方法允许使用高效的自动数据挖掘工具来快速筛选大量候选变量来表征热点。 一个应用是筛选将疑似热点与参考集区分开的变量。
    • 12. 发明申请
    • METHOD AND COMPUTER PROGRAM PRODUCT FOR USING DATA MINING TOOLS TO AUTOMATICALLY COMPARE AN INVESTIGATED UNIT AND A BENCHMARK UNIT
    • 使用数据挖掘工具自动比较被调查单位和基准单位的方法和计算机程序产品
    • US20090112917A1
    • 2009-04-30
    • US12251750
    • 2008-10-15
    • James Howard Drew
    • James Howard Drew
    • G06F17/30
    • G06Q90/00G06Q10/0639Y10S707/99936
    • Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.
    • 业务交易中的运营问题来源常常显示在相对较小的数据中,这就是所谓的故障热点。 从内部公司交易数据中识别这些热点通常是问题解决的基本步骤,但是由于大量的事务和大量的事务变量要分析,这个分析过程非常复杂。 提供了一套实用的修改,用于数据挖掘技术和逻辑回归,以定制他们查找故障热点。 因此,这种方法允许使用高效的自动数据挖掘工具来快速筛选大量候选变量来表征热点。 一个应用是筛选将疑似热点与参考集区分开的变量。