Abstract: In an approach to generating quantitatively assessed synthetic training data, one or more computer processors identify an initial plurality of clusters in a dataset utilizing a trained classification model and a plurality of associated hyperparameters, wherein the clusters have sufficient density to be represented in a calculated probability distribution. The one or more computer processors generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution. The one or more computer processors quantitatively assess the one or more generated synthetic data points.

Abstract: Identifying groupings of individual users of a mobile network. In particular, some embodiments are configured to obtain transaction data records from one or more mobile network provider(s). Groupings of users may be identified by capturing association patterns in the data by applying word embedding.

Abstract: In an approach to generating quantitatively assessed synthetic training data, one or more computer processors identify an initial plurality of clusters in a dataset utilizing a trained classification model and a plurality of associated hyperparameters, wherein the clusters have sufficient density to be represented in a calculated probability distribution. The one or more computer processors generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution. The one or more computer processors quantitatively assess the one or more generated synthetic data points.

Abstract: Identifying groupings of individual users of a mobile network. In particular, some embodiments are configured to obtain transaction data records from one or more mobile network provider(s). Groupings of users may be identified by capturing association patterns in the data by applying word embedding.