Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.

Abstract: Systems and methods for document generation are provided. One aspect of the systems and methods includes identifying, by a style extractor, a document fragment comprising a first style element of a first style category; computing, by a style generator, a reward function based on a correlation value between the first style element and a second style element of a second style category different from the first style category, wherein the correlation value is based on correlations between style elements in a plurality of historical document fragments; selecting, by the style generator, the second style element based on the reward function; and generating, by a document generator, a modified document fragment that includes the first style element of the first style category and the second style element of the second style category.

Abstract: Systems and methods for data processing are described. Example embodiments include identifying chart data corresponding to a visual element of a user interface; selecting an insight type based on a chart category of the chart data; generating insight data for the insight type based on the chart data using a statistical measure corresponding to the insight type; generating an insight caption for the insight type by combining the insight data with a sentence template corresponding to the insight type; and communicating the insight caption to a user of the user interface.

Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Abstract: A method for generating a graphical display of a bipartite graph includes receiving bipartite graph data, generating, a first meta-node including at least two nodes in the first set of nodes in the bipartite graph data and a second meta-node including at least two nodes in a second set of nodes in the bipartite graph data based on the bipartite graph data using a minimum description length (MDL) optimization process to generate the first meta-node and the second meta-node. The method further includes generating a first graphical depiction of the first meta-node and the second meta-node, the graphical depiction including a single edge connecting the first meta-node and the second meta-node to provide a summarized display of the bipartite graph data.

Abstract: A method for generating a graphical display of a bipartite graph includes receiving bipartite graph data, generating, a first meta-node including at least two nodes in the first set of nodes in the bipartite graph data and a second meta-node including at least two nodes in a second set of nodes in the bipartite graph data based on the bipartite graph data using a minimum description length (MDL) optimization process to generate the first meta-node and the second meta-node. The method further includes generating a first graphical depiction of the first meta-node and the second meta-node, the graphical depiction including a single edge connecting the first meta-node and the second meta-node to provide a summarized display of the bipartite graph data.