Utilizing machine learning to detect single and cluster-type anomalies in a data set

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, configured to:
• receive unlabeled data associated with a particular domain;
• select sets of data from the unlabeled data;
• calculate Gaussian kernel densities and minimum distances for data points in each of the sets of data;
• calculate anomaly scores for the data points in each of the sets of data based on the Gaussian kernel densities and the minimum distances for the data points in each of the sets of data;
• process the anomaly scores for the data points in each of the sets of data, with a machine learning model, to determine a single anomaly score for the data points in each of the sets of data,
• wherein a plurality of single anomaly scores is determined for the sets of data;
• calculate a final anomaly score for the unlabeled data based on a combination of the plurality of single anomaly scores;
• and
• perform one or more actions based on the final anomaly score.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive unlabeled data associated with a particular domain;
• select sets of data from the unlabeled data;
• calculate Gaussian kernel densities and minimum distances for data points in each of the sets of data;
• calculate anomaly scores for the data points in each of the sets of data based on the Gaussian kernel densities and the minimum distances for the data points in each of the sets of data;
• train a machine learning model, with the anomaly scores for the data points in each of the sets of data, to generate a trained machine learning model that determines a single anomaly score for the data points in each of the sets of data,
• wherein a plurality of single anomaly scores is determined for the sets of data;
• identify anomalous data points in the unlabeled data based on the plurality of single anomaly scores;
• calculate a final anomaly score for the unlabeled data based on a combination of the plurality of single anomaly scores;
• and
• perform one or more actions based on the final anomaly score and the anomalous data points.