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: 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: The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently predicting a persona class of a client device and/or target user utilizing an overlap-agnostic machine learning model and distributing persona-based digital content to the client device. In particular, in one or more embodiments, the persona classification system can learn overlap-agnostic machine learning model parameters to apply to user traits in real-time or in offline batches. For example, the persona classification system can train and utilize an overlap-agnostic machine learning model that includes an overlap-agnostic embedding model, a trained user-embedding generation model, and a trained persona prediction model. By applying the learned overlap-agnostic machine learning model parameters to the target user traits, the persona classification system can predict a persona class for sending digital content based on the predicted persona class.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.