Walmart Inc.
Relative density-based clustering and anomaly detection system

Abstract:

Examples provide a system for detecting anomalies in a dataset. The system includes one or more processors and a memory storing the dataset. The one or more processors are programmed to identify a first set of data points in a cluster, identify a second set of data points outside of the cluster as noisy data points, and determine whether each of the noisy data points is an anomaly by: determining a distance between the noisy data point and other data points in the dataset, ranking the distances between the noisy data point and the other data points, and applying a weight to each of the ranked distances to determine an outlier value for the noisy data point. When the outlier value for the noisy data point exceeds a threshold, the noisy data point is identified as an anomaly, and result is displayed in a user interface.