ATTRIBUTE DIVERSITY FOR FREQUENT PATTERN ANALYSIS

    公开(公告)号:US20200301966A1

    公开(公告)日:2020-09-24

    申请号:US16355996

    申请日:2019-03-18

    Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

    Attribute diversity for frequent pattern analysis

    公开(公告)号:US11556595B2

    公开(公告)日:2023-01-17

    申请号:US17163081

    申请日:2021-01-29

    Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

    ATTRIBUTE DIVERSITY FOR FREQUENT PATTERN ANALYSIS

    公开(公告)号:US20210157847A1

    公开(公告)日:2021-05-27

    申请号:US17163081

    申请日:2021-01-29

    Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

    CORRELATING USER DEVICE ATTRIBUTE GROUPS
    4.
    发明申请

    公开(公告)号:US20200311143A1

    公开(公告)日:2020-10-01

    申请号:US16370360

    申请日:2019-03-29

    Abstract: A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

    Correlating user device attribute groups

    公开(公告)号:US11238105B2

    公开(公告)日:2022-02-01

    申请号:US16370360

    申请日:2019-03-29

    Abstract: A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

    Attribute diversity for frequent pattern analysis

    公开(公告)号:US10963519B2

    公开(公告)日:2021-03-30

    申请号:US16355996

    申请日:2019-03-18

    Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

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