Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).

Abstract: Systems and methods for identifying data files that have a common characteristic are provided. A plurality of data files are received. The plurality of data files include one or more data files having the common characteristic. A list of key terms is generated from the plurality of data files. Data files from the plurality of data files that have an association with a social community are identified, where the social community is defined by one or more features. The list of key terms is updated based on an analysis of the identified features. The updated list of key terms is used to identify other data files that have the common characteristic.

Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).

Abstract: Computer-implemented systems and methods for constructing decision trees to predict target values. A method can include receiving data containing descriptive data and target values. The target values are partitioned into groups. A rule induction engine generates a set of rules for each group based upon a set of descriptive data. The rules are configured to predict target values. In a bottom-up manner, a decision tree is generated for each of the groups.