US 12,292,902 B2
Apparatus and method for data clustering
Changhee Lee, Seoul (KR)
Assigned to CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION, Seoul (KR)
Filed by CHUNG ANG University industry Academic Cooperation Foundation, Seoul (KR)
Filed on Apr. 9, 2024, as Appl. No. 18/630,084.
Claims priority of application No. 10-2023-0047068 (KR), filed on Apr. 10, 2023; and application No. 10-2023-0047069 (KR), filed on Apr. 10, 2023.
Prior Publication US 2024/0338388 A1, Oct. 10, 2024
Int. Cl. G06F 7/00 (2006.01); G06F 16/22 (2019.01); G06F 16/2458 (2019.01); G06F 16/28 (2019.01)
CPC G06F 16/285 (2019.01) [G06F 16/2237 (2019.01); G06F 16/2477 (2019.01)] 12 Claims
OG exemplary drawing
 
1. A method for data clustering performed in a computing device including one or more processors and a memory that stores one or more programs executed by the one or more processors, the method comprising:
receiving two or more multivariate discrete time series data as input and generating an embedding vector for each of the multivariate discrete time series data using a first artificial neural network;
generating a similarity graph by performing a path-based connectivity test between embedding vectors for each of the two or more multivariate discrete time series data;
predicting a label distribution for embedding vectors for each of the two or more multivariate discrete time series data using a second artificial neural network; and
clustering the two or more multivariate discrete time series data based on the similarity graph and the label distribution,
wherein the first artificial neural network generates an embedding vector consisting of poles and coefficients of Laplace transform for each of the two or more multivariate discrete time series data, and
the first artificial neural network is trained to minimize a loss function generated based on a difference between a time function of each of the two or more multivariate discrete time series data generated by performing inverse Laplace transform using the poles and the coefficients and the two or more multivariate discrete time series data.