Abstract: Methods, systems, and devices for displaying a user interface for frequent pattern (FP) analysis are described. In some cases, data stored at a multi-tenant database server may be analyzed to understand various interactions and patterns between data attributes associated with multiple users, or determine one or more attributes associated with a characterization of an individual (e.g., a persona). The multi-tenant database server may effectively cluster and/or perform calculations on attributes of the data to understand user patterns and determine common personas. The results may then be displayed by a user interface at a user device (e.g., associated with the user).

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.

Abstract: Methods, systems, and devices supporting differential support for frequent pattern (FP) analysis are described. Some database systems may analyze data sets to determine FPs of data attributes within the data sets. However, if data distributions for different types of data attributes vary greatly, more frequent data attribute types may skew the FPs away from the less frequent types. To reduce the noise of common attributes while maintaining sensitivity to the less common attributes, the database system may implement multiple minimum support (e.g., frequency) thresholds. For example, the database system may adaptively categorize the different data attribute types into data categories based on their distributions and may dynamically determine support thresholds for the categories. Using different minimum support thresholds for different data categories allows the system to filter out data attribute patterns based on the distributions of the data attribute types in the pattern.

Abstract: A database system performs lookalike analysis on a data set including a plurality of user identifiers, which are associated with one or more attribute records. The database system classifies the user identifiers into one or more segments of user identifiers based on the attribute records. The database system performs Linear Discriminant Analysis (LDA) to calculate a measure of importance of the attribute records relative to the one or more segments. The database system auto-correlates the attribute records based on the numbers of attribute records in the user identifier population and the one or more segments. The database system identifies a set of user identifiers relative to one or more segments using the measures of importance and the auto-correlated parameters.

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.

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.

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.

Abstract: A database system performs lookalike analysis on a data set including a plurality of user identifiers, which are associated with one or more attribute records. The database system classifies the user identifiers into one or more segments of user identifiers based on the attribute records. The database system performs Linear Discriminant Analysis (LDA) to calculate a measure of importance of the attribute records relative to the one or more segments. The database system auto-correlates the attribute records based on the numbers of attribute records in the user identifier population and the one or more segments. The database system identifies a set of user identifiers relative to one or more segments using the measures of importance and the auto-correlated parameters.

Abstract: Methods, systems, and devices for displaying a user interface for frequent pattern (FP) analysis are described. In some cases, data stored at a multi-tenant database server may be analyzed to understand various interactions and patterns between data attributes associated with multiple users, or determine one or more attributes associated with a characterization of an individual (e.g., a persona). The multi-tenant database server may effectively cluster and/or perform calculations on attributes of the data to understand user patterns and determine common personas. The results may then be displayed by a user interface at a user device (e.g., associated with the user).

Abstract: Methods, systems, and devices supporting differential support for frequent pattern (FP) analysis are described. Some database systems may analyze data sets to determine FPs of data attributes within the data sets. However, if data distributions for different types of data attributes vary greatly, more frequent data attribute types may skew the FPs away from the less frequent types. To reduce the noise of common attributes while maintaining sensitivity to the less common attributes, the database system may implement multiple minimum support (e.g., frequency) thresholds. For example, the database system may adaptively categorize the different data attribute types into data categories based on their distributions and may dynamically determine support thresholds for the categories. Using different minimum support thresholds for different data categories allows the system to filter out data attribute patterns based on the distributions of the data attribute types in the pattern.