Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information

Autor: Helen Pinto, Ian Gates, Xin Wang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: MethodsX, Vol 7, Iss , Pp 100897- (2020)
Druh dokumentu: article
ISSN: 2215-0161
DOI: 10.1016/j.mex.2020.100897
Popis: Bayesian Biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [1]. In this companion paper the BBCD algorithm is tested on synthetic data, demonstrating use of the algorithm, as well as its robustness, and performance accuracy against Random Agglomeration. Supplementary information includes formulae to calculate analytical steam and oil volume data used as background knowledge for the SAGD application. Advantages of the BBCD algorithm are listed below. • It includes background knowledge directly into the clustering process. • It finds similarity between series and over time. • It allows a user-specified definition for behaviour of interest, which relaxes dependency on series shape. This is important when similar behavioural events do not necessarily occur in the same temporal order.
Databáze: Directory of Open Access Journals