Reducing utilization of computational resources associated with segmenting datasets via a cluster-ensemble model systems and methods

A method for reducing utilization of computational resources associated with segmenting datasets via a cluster-ensemble model, the method comprising:

• receiving a raw dataset having a first dimension comprising entity identifiers associated with entities that users have interacted with and timestamps at which the users interacted with the entities;
• extracting, from the raw dataset, the entity identifiers associated with the entities that the users have interacted with;
• determining, for each entity identifier of the entity identifiers, a value associated with a size of the entity associated with the entity identifier;
• in response to the value associated with the size of the entity exceeding a threshold value, removing information from the raw dataset that corresponds to the entity, wherein the value associated with the size of the entity exceeds the threshold value;
• embedding the raw dataset into an embedded dataset having a second dimension, wherein the embedded dataset comprises vector embeddings of the entity identifiers and the timestamps at which the users interacted with the entities;
• providing the embedded dataset as input to a set of clustering models to generate a set of clusters, the set of clusters being based on distances between the vector embeddings of a subset of the embedded dataset, wherein each cluster of the set of clusters corresponds to a respective clustering model of the set of clustering models;
• providing the set of clusters to the cluster-ensemble model to generate a set of ensemble-clusters;
• and
• generating, based on the set of ensemble-clusters, a set of data segments corresponding to the set of ensemble-clusters indicating at least one characteristic of a respective ensemble-cluster of the set of ensemble-clusters.

One or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising:

• embedding a raw dataset having a first dimension into an embedded dataset having a second dimension, wherein the embedded dataset comprises vector embeddings of entity identifiers associated with entities and timestamps at which users interacted with the entities;
• providing the embedded dataset to a set of clustering models to generate a set of clusters, the set of clusters being based on determined distances between the vector embeddings of a subset of the embedded dataset, wherein each cluster of the set of clusters corresponds to a respective clustering model of the set of clustering models;
• providing the set of clusters to a cluster-ensemble model to generate a set of ensemble-clusters;
• and
• generating, based on the set of ensemble-clusters, a set of data segments corresponding to the set of ensemble-clusters, wherein a first data segment of the set of data segments is generated via:
• determining (i) an entity characteristic associated with each entity of the entities of the raw dataset and (ii) a first frequency associated with the entity characteristic;
• determining, for each cluster of the set of ensemble-clusters, whether a second frequency associated with a second entity characteristic of the entities that are part of the respective ensemble-cluster meets or exceeds the first frequency for corresponding entities;
• and
• in response to the second frequency meeting or exceeding the first frequency, generating the first data segment, wherein the second frequency meets or exceeds the first frequency.