Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(< n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.
['Sungwon Jung', 'Kwang Hyung Lee', 'Doheon Lee']
I am writing a research paper on a idea Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality
[{"from": "human", "value": "I am writing a research paper on a idea Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(< n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available."}]