US 12,423,330 B2
Updating data models to manage data drift and outliers
Satish Kumar Mopur, Bangalore (IN); Sridhar Balachandriah, Bangalore (IN); Gunalan Perumal Vijayan, Bangalore (IN); Suresh Ladapuram Soundararajan, Bangalore (IN); and Krishna Prasad Lingadahalli Shastry, Bangalore (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP, Spring, TX (US)
Filed on Mar. 18, 2024, as Appl. No. 18/607,923.
Application 18/607,923 is a continuation of application No. 17/225,805, filed on Apr. 8, 2021, granted, now 11,954,129.
Claims priority of application No. 202041021065 (IN), filed on May 19, 2020.
Prior Publication US 2024/0220514 A1, Jul. 4, 2024
Int. Cl. G06F 16/28 (2019.01); G06F 18/214 (2023.01); G06F 18/2321 (2023.01); G06F 18/23213 (2023.01); G06F 18/2413 (2023.01); G06F 18/2433 (2023.01)
CPC G06F 16/285 (2019.01) [G06F 18/214 (2023.01); G06F 18/2321 (2023.01); G06F 18/23213 (2023.01); G06F 18/24137 (2023.01); G06F 18/2433 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
determining a plurality of representative points of input data using different data techniques;
upon identifying deviations between clusters of the plurality of representative points, modifying a defined segment size as a modified segment size, the defined segment size and the modified segment size being used to segment the input data;
obtaining a plurality of new representative points corresponding to new segments of the input data using the modified segment size;
clustering the new representative points using at least two different data clustering techniques, the clustering obtaining multiple sets of new clusters of the plurality of new representative points;
eliminating deviations between the multiple sets of the new clusters;
repeating the obtaining, the clustering, and the eliminating until the plurality of new representative points is within a threshold; and
training a data model using the input data with the plurality of new representative points.