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Systems and methods for multivariate influence analysis of heterogenous mixtures of categorical and continuous data

机译:用于分类数据和连续数据的异构混合物的多元影响分析的系统和方法

摘要

Systems, methods, and computer readable storage medium with executable instructions for detecting outliers and hidden relationships in heterogeneous data sets are provided. Features of the invention pertain to design and operation of various predictive models that identify multivariate outliers and influential observations by recognizing systematic local relationships within heterogeneous data sets or subpopulations of heterogeneous data sets. Multivariate outliers and influential observations are identified by utilizing general distance metrics which are specific to and defined for any number of individual observations within heterogeneous data sets. Aspects of the invention may be applied to sets of data that are large and complex (e.g. loan portfolios, health insurance company data, homeland security profiles, etc.) or sets of data having a more-limited scope (e.g. medical or drug research, etc.).
机译:提供了具有用于检测异构数据集中的异常值和隐藏关系的可执行指令的系统,方法和计算机可读存储介质。本发明的特征涉及各种预测模型的设计和操作,所述预测模型通过识别异类数据集或异类数据集的子群内的系统局部关系来识别多元离群值和有影响的观察结果。多变量离群值和有影响力的观测值是通过利用通用距离度量来识别的,该度量标准是异类数据集中特定数量的个体观测值并为之定义的。本发明的各方面可以应用于大型且复杂的数据集(例如,贷款组合,健康保险公司数据,国土安全概况等)或范围更有限的数据集(例如,医学或药物研究,等等。)。

著录项

  • 公开/公告号US8065247B2

    专利类型

  • 公开/公告日2011-11-22

    原文格式PDF

  • 申请/专利权人 ALAN SCHLOTTMANN;

    申请/专利号US20080115409

  • 发明设计人 ALAN SCHLOTTMANN;

    申请日2008-05-05

  • 分类号G06E1/00;G06E3/00;G06F15/18;G06G7/00;

  • 国家 US

  • 入库时间 2022-08-21 17:26:10

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