Abstract
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.
Highlights
The Bayesian Biclustering by Dynamics (BBCD) algorithm [1] is an extension of the Bayesian Clustering by Dynamics (BCD) algorithm [2], created to address the needs of a specific application in Steam Assisted Gravity Drainage (SAGD) oil recovery
The accompanying research paper [1] explains how this algorithm works with Steam-Assisted Gravity Drainage (SAGD) data, the results found, and a domain-specific interpretation of results
This paper describes synthetic data testing done to ensure that the BBCD algorithm is accurate and robust to noise
Summary
Computer Science An algorithm that biclusters time-series data structured as Bayesian matrices, which makes it easier to interpret the resulting clusters. Testing follows the approach taken in [3] where specific biclustered data sets are created to demonstrate algorithm performance under different testing scenarios. Nodes contain transition probability matrices that are the base data structure of the algorithm.
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